1845 Mlflow Jobs - Page 20

Location: Kolkata, India Job Type: Full-time Salary: ₹20,000 - ₹30,000 per month (negotiable based on experience) Experience: 2 to 3 years About the Role: We are seeking a highly motivated and skilled AI/ML Engineer to join our growing team. As part of our innovation-driven environment, you will be responsible for designing, developing, and deploying cutting-edge machine learning models and AI-driven applications that solve real-world problems and deliver business value. Key Responsibilities: Build, train, and deploy machine learning and deep learning models Work with large and complex datasets (structured and unstructured) Collaborate with data scientists, analysts, and software engineers to integrate ML models into production systems Conduct research and experiments to explore new AI/ML approaches Develop scalable pipelines for data preprocessing, model training, and inference Optimize models for accuracy, performance, and efficiency Document processes and present findings to stakeholders Required Skills & Qualifications: · Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field · 2–5 years of experience in machine learning, deep learning, or data science · Proficiency in Python and libraries such as TensorFlow, PyTorch, scikit-learn, Pandas, NumPy · Experience with ML lifecycle tools (e.g., MLflow, Kubeflow, AWS SageMaker) · Understanding of model evaluation metrics, overfitting, cross-validation, etc. · Familiarity with NLP, Computer Vision, or Time Series models is a plus · Strong problem-solving and analytical skills Preferred Skills (Bonus): · Experience with cloud platforms (AWS, GCP, Azure) · Exposure to big data technologies (Spark, Hadoop) · Working knowledge of MLOps, CI/CD pipelines for ML · Knowledge of generative AI (LLMs, diffusion models, etc.) Why Join Us: · Work on impactful AI/ML projects across industries · Exposure to real-time production environments · Supportive, collaborative team culture · Opportunities for learning and certifications · Competitive salary and benefits Perks & Benefits: Competitive salary (₹20,000 - ₹30,000 per month based on experience) Leadership opportunity in a fast-growing tech team Work on challenging, large-scale projects Career growth, team expansion, and long-term stability Skill development & mentorship opportunities Supportive and collaborative work culture How to Apply: If you’re ready to take the next step in your career, send your resume and portfolio to info@mindpik.co.in or apply directly at: https://mindpik.co.in Job Types: Full-time, Permanent Pay: ₹30,000.00 - ₹30,000.00 per month (Negotiable) Benefits: Paid sick/casual Leave Team leadership experience Career advancement Location Type: In-person Schedule: Day shift Work Location: In person Job Types: Full-time, Permanent Pay: ₹17,826.00 - ₹32,300.40 per month Benefits: Paid sick time Location Type: In-person Schedule: Day shift Work Location: In person Speak with the employer +91 9811500219 Expected Start Date: 21/07/2025

Job Title: DevOps/MLOps Expert Location: Mohali (On-Site) Employment Type: Full-Time Experience: 6 + years Qualification: B.Tech CSE About the Role We are seeking a highly skilled DevOps/MLOps Expert to join our rapidly growing AI-based startup building and deploying cutting-edge enterprise AI/ML solutions. This is a critical role that will shape our infrastructure, deployment pipelines, and scale our ML operations to serve large-scale enterprise clients. As our DevOps/MLOps Expert , you will be responsible for bridging the gap between our AI/ML development teams and production systems, ensuring seamless deployment, monitoring, and scaling of our ML-powered enterprise applications. You’ll work at the intersection of DevOps, Machine Learning, and Data Engineering in a fast-paced startup environment with enterprise-grade requirements. Key Responsibilities MLOps & Model Deployment • Design, implement, and maintain end-to-end ML pipelines from model development to production deployment • Build automated CI/CD pipelines specifically for ML models using tools like MLflow, Kubeflow, and custom solutions • Implement model versioning, experiment tracking, and model registry systems • Monitor model performance, detect drift, and implement automated retraining pipelines • Manage feature stores and data pipelines for real-time and batch inference • Build scalable ML infrastructure for high-volume data processing and analytics Enterprise Cloud Infrastructure & DevOps • Architect and manage cloud-native infrastructure with focus on scalability, security, and compliance • Implement Infrastructure as Code (IaC) using Terraform , CloudFormation , or Pulumi • Design and maintain Kubernetes clusters for containerized ML workloads • Build and optimize Docker containers for ML applications and microservices • Implement comprehensive monitoring, logging, and alerting systems • Manage secrets, security, and enterprise compliance requirements Data Engineering & Real-time Processing • Build and maintain large-scale data pipelines using Apache Airflow , Prefect , or similar tools • Implement real-time data processing and streaming architectures • Design data storage solutions for structured and unstructured data at scale • Implement data validation, quality checks, and lineage tracking • Manage data security, privacy, and enterprise compliance requirements • Optimize data processing for performance and cost efficiency Enterprise Platform Operations • Ensure high availability (99.9%+) and performance of enterprise-grade platforms • Implement auto-scaling solutions for variable ML workloads • Manage multi-tenant architecture and data isolation • Optimize resource utilization and cost management across environments • Implement disaster recovery and backup strategies • Build 24x7 monitoring and alerting systems for mission-critical applications Required Qualifications Experience & Education • 4-8 years of experience in DevOps/MLOps with at least 2+ years focused on enterprise ML systems • Bachelor’s/Master’s degree in Computer Science, Engineering, or related technical field • Proven experience with enterprise-grade platforms or large-scale SaaS applications • Experience with high-compliance environments and enterprise security requirements • Strong background in data-intensive applications and real-time processing systems Technical Skills Core MLOps Technologies • ML Frameworks : TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost • MLOps Tools : MLflow, Kubeflow, Metaflow, DVC, Weights & Biases • Model Serving : TensorFlow Serving, PyTorch TorchServe, Seldon Core, KFServing • Experiment Tracking : MLflow, Neptune.ai, Weights & Biases, Comet DevOps & Cloud Technologies • Cloud Platforms : AWS, Azure, or GCP with relevant certifications • Containerization : Docker, Kubernetes (CKA/CKAD preferred) • CI/CD : Jenkins, GitLab CI, GitHub Actions, CircleCI • IaC : Terraform, CloudFormation, Pulumi, Ansible • Monitoring : Prometheus, Grafana, ELK Stack, Datadog, New Relic Programming & Scripting • Python (advanced) - primary language for ML operations and automation • Bash/Shell scripting for automation and system administration • YAML/JSON for configuration management and APIs • SQL for data operations and analytics • Basic understanding of Go or Java (advantage) Data Technologies • Data Pipeline Tools : Apache Airflow, Prefect, Dagster, Apache NiFi • Streaming & Real-time : Apache Kafka, Apache Spark, Apache Flink, Redis • Databases : PostgreSQL, MongoDB, Elasticsearch, ClickHouse • Data Warehousing : Snowflake, BigQuery, Redshift, Databricks • Data Versioning : DVC, LakeFS, Pachyderm Preferred Qualifications Advanced Technical Skills • Enterprise Security : Experience with enterprise security frameworks, compliance (SOC2, ISO27001) • High-scale Processing : Experience with petabyte-scale data processing and real-time analytics • Performance Optimization : Advanced system optimization, distributed computing, caching strategies • API Development : REST/GraphQL APIs, microservices architecture, API gateways Enterprise & Domain Experience • Previous experience with enterprise clients or B2B SaaS platforms • Experience with compliance-heavy industries (finance, healthcare, government) • Understanding of data privacy regulations (GDPR, SOX, HIPAA) • Experience with multi-tenant enterprise architectures Leadership & Collaboration • Experience mentoring junior engineers and technical team leadership • Strong collaboration with data science teams , product managers , and enterprise clients • Experience with agile methodologies and enterprise project management • Understanding of business metrics , SLAs , and enterprise ROI Growth Opportunities • Career Path : Clear progression to Lead DevOps Engineer or Head of Infrastructure • Technical Growth : Work with cutting-edge enterprise AI/ML technologies • Leadership : Opportunity to build and lead the DevOps/Infrastructure team • Industry Exposure : Work with Government & MNCs enterprise clients and cutting-edge technology stacks Success Metrics & KPIs Technical KPIs • System Uptime : Maintain 99.9%+ availability for enterprise clients • Deployment Frequency : Enable daily deployments with zero downtime • Performance : Ensure optimal response times and system performance • Cost Optimization : Achieve 20-30% annual infrastructure cost reduction • Security : Zero security incidents and full compliance adherence Business Impact • Time to Market : Reduce deployment cycles and improve development velocity • Client Satisfaction : Maintain 95%+ enterprise client satisfaction scores • Team Productivity : Improve engineering team efficiency by 40%+ • Scalability : Support rapid client base growth without infrastructure constraints Why Join Us Be part of a forward-thinking, innovation-driven company with a strong engineering culture. Influence high-impact architectural decisions that shape mission-critical systems. Work with cutting-edge technologies and a passionate team of professionals. Competitive compensation, flexible working environment, and continuous learning opportunities. How to Apply Please submit your resume and a cover letter outlining your relevant experience and how you can contribute to Aaizel Tech Labs’ success. Send your application to hr@aaizeltech.com , bhavik@aaizeltech.com or anju@aaizeltech.com.

🚀 We’re Hiring: Generative AI Architect | Noida (Hybrid) Experience: 10+ years (with 2–3 years in GenAI/LLMs) Type: Full-Time | Hybrid (Noida) Are you passionate about shaping the future of AI? We’re on the lookout for a Generative AI Architect to lead the design, development, and deployment of next-generation GenAI solutions that power enterprise-scale applications. This is your opportunity to work at the intersection of AI innovation and real-world impact—building intelligent systems using cutting-edge models like GPT, Claude, LLaMA, and Mistral. 🔍 What You’ll Do: Architect and implement secure, scalable GenAI solutions using LLMs. Design state-of-the-art RAG pipelines using LangChain, LlamaIndex, FAISS, etc. Lead prompt engineering and build reusable modules (e.g., chatbots, summarizers). Deploy on cloud-native platforms: AWS Bedrock, Azure OpenAI, GCP Vertex AI . Integrate GenAI into enterprise products in collaboration with cross-functional teams. Drive MLOps best practices for CI/CD, monitoring, and observability. Explore the frontier of GenAI—multi-agent systems, fine-tuning, and autonomous agents. Ensure compliance, security, and data governance in all AI systems. ✅ What We’re Looking For: 8+ years in AI/ML, with 2–3 years in LLMs or GenAI. Proficiency in Python , Transformers, LangChain, OpenAI SDKs. Experience with Vector Databases (Pinecone, Weaviate, FAISS). Hands-on with cloud platforms: AWS, Azure, GCP. Knowledge of LLM orchestration (LangGraph, AutoGen, CrewAI). Familiarity with tools like MLflow, Docker, Kubernetes, FastAPI. Strong understanding of GenAI evaluation metrics (BERTScore, BLEU, GPTScore). Excellent communication and architectural leadership skills. 🌟 Nice to Have: Experience fine-tuning open-source LLMs (LoRA, QLoRA). Exposure to multi-modal AI systems (text-image, speech). Domain knowledge in BFSI, Healthcare, Legal, or EdTech. Published research or open-source contributions in GenAI. 📍 Location: Noida (Hybrid) 🌐 Apply Now and be part of the GenAI transformation. Let’s build the future—one intelligent system at a time. 💡 #GenerativeAI #LLM #AIArchitect #MachineLearning #LangChain #VertexAI #GenAIJobs #PromptEngineering #Hiring #TechJobs #AIInnovation

:-Job Title: Machine Learning Engineer – 2 Location: Onsite – Bengaluru, Karnataka, India Experience Required: 3 – 6 Years Compensation: ₹20 – ₹25 LPA Employment Type: Full-Time Work Mode: Onsite Only (No Remote) About the Company:- A fast-growing Y Combinator-backed SaaS startup is revolutionizing underwriting in the insurance space through AI and Generative AI. Their platform empowers insurance carriers in the U.S. to make faster, more accurate decisions by automating key processes and enhancing risk assessment. As they expand their AI capabilities, they’re seeking a Machine Learning Engineer – 2 to build scalable ML solutions using NLP, Computer Vision, and LLM technologies. Role Overview:- As a Machine Learning Engineer – 2, you'll take ownership of designing, developing, and deploying ML systems that power critical features across the platform. You'll lead end-to-end ML workflows, working with cross-functional teams to deliver real-world AI solutions that directly impact business outcomes. Key Responsibilities:- Design and develop robust AI product features aligned with user and business needs Maintain and enhance existing ML/AI systems Build and manage ML pipelines for training, deployment, monitoring, and experimentation Deploy scalable inference APIs and conduct A/B testing Optimize GPU architectures and fine-tune transformer/LLM models Build and deploy LLM applications tailored to real-world use cases Implement DevOps/ML Ops best practices with tools like Docker and Kubernetes Tech Stack & Tools Machine Learning & LLMs GPT, LLaMA, Gemini, Claude, Hugging Face Transformers PyTorch, TensorFlow, Scikit-learn LLMOps & MLOps Langchain, LangGraph, LangFlow, Langfuse MLFlow, SageMaker, LlamaIndex, AWS Bedrock, Azure AI Cloud & Infrastructure AWS, Azure Kubernetes, Docker Databases MongoDB, PostgreSQL, Pinecone, ChromaDB Languages Python, SQL, JavaScript What You’ll Do Collaborate with product, research, and engineering teams to build scalable AI solutions Implement advanced NLP and Generative AI models (e.g., RAG, Transformers) Monitor and optimize model performance and deployment pipelines Build efficient, scalable data and feature pipelines Stay updated on industry trends and contribute to internal innovation Present key insights and ML solutions to technical and business stakeholders Requirements Must-Have:- 3–6 years of experience in Machine Learning and software/data engineering Master’s degree (or equivalent) in ML, AI, or related technical fields Strong hands-on experience with Python, PyTorch/TensorFlow, and Scikit-learn Familiarity with ML Ops, model deployment, and production pipelines Experience working with LLMs and modern NLP techniques Ability to work collaboratively in a fast-paced, product-driven environment Strong problem-solving and communication skills Bonus Certifications such as: AWS Machine Learning Specialty AWS Solution Architect – Professional Azure Solutions Architect Expert Why Apply Work directly with a high-caliber founding team Help shape the future of AI in the insurance space Gain ownership and visibility in a product-focused engineering role Opportunity to innovate with state-of-the-art AI/LLM tech Be part of a fast-moving team with real market traction 📍 Note: This is an onsite-only role based in Bengaluru. Remote work is not available. Skills: mongodb,pytorch,aws,javascript,python,azure,llms and modern nlp techniques,computer vision,chromadb,tensorflow,docker,scikit-learn,mlops,ml ops,kubernetes,ml, ai,nlp,llm,software/data engineering,python, pytorch/tensorflow, and scikit-learn,pinecone,postgresql,machine learning,llms,llm technologies,sql,devops

Job Title: Senior Test Automation Lead – Playwright (AI/ML Focus) Location: Hyderabad Job Type: Full-Time Experience Required: 8+ years in Software QA/Testing, 3+ years in Test Automation using Playwright, 2+ years in AI/ML project environments --- About the Role: We are seeking a passionate and technically skilled Senior Test Automation Lead with deep experience in Playwright-based frameworks and a solid understanding of AI/ML-driven applications. In this role, you will lead the automation strategy and quality engineering practices for next-generation AI products that integrate large-scale machine learning models, data pipelines, and dynamic, intelligent UIs. You will define, architect, and implement scalable automation solutions across AI-enhanced features such as recommendation engines, conversational UIs, real-time analytics, and predictive workflows, ensuring both functional correctness and intelligent behavior consistency. --- Key Responsibilities: Test Automation Framework Design & Implementation · Design and implement robust, modular, and extensible Playwright automation frameworks using TypeScript/JavaScript. · Define automation design patterns and utilities that can handle complex AI-driven UI behaviors (e.g., dynamic content, personalization, chat interfaces). · Implement abstraction layers for easy test data handling, reusable components, and multi-browser/platform execution. AI/ML-Specific Testing Strategy · Partner with Data Scientists and ML Engineers to understand model behaviors, inference workflows, and output formats. · Develop strategies for testing non-deterministic model outputs (e.g., chat responses, classification labels) using tolerance ranges, confidence intervals, or golden datasets. · Design tests to validate ML integration points: REST/gRPC API calls, feature flags, model versioning, and output accuracy. · Include bias, fairness, and edge-case validations in test suites where applicable (e.g., fairness in recommendation engines or NLP sentiment analysis). End-to-End Test Coverage · Lead the implementation of end-to-end automation for: o Web interfaces (React, Angular, or other SPA frameworks) o Backend services (REST, GraphQL, WebSockets) o ML model integration endpoints (real-time inference APIs, batch pipelines) · Build test utilities for mocking, stubbing, and simulating AI inputs and datasets. CI/CD & Tooling Integration · Integrate automation suites into CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI, or similar. · Configure parallel execution, containerized test environments (e.g., Docker), and test artifact management. · Establish real-time dashboards and historical reporting using tools like Allure, ReportPortal, TestRail, or custom Grafana integrations. Quality Engineering & Leadership · Define KPIs and QA metrics for AI/ML product quality: functional accuracy, model regression rates, test coverage %, time-to-feedback, etc. · Lead and mentor a team of automation and QA engineers across multiple projects. · Act as the Quality Champion across the AI platform by influencing engineering, product, and data science teams on quality ownership and testing best practices. Agile & Cross-Functional Collaboration · Work in Agile/Scrum teams; participate in backlog grooming, sprint planning, and retrospectives. · Collaborate across disciplines: Frontend, Backend, DevOps, MLOps, and Product Management to ensure complete testability. · Review feature specs, AI/ML model update notes, and data schemas for impact analysis. --- Required Skills and Qualifications: Technical Skills: · Strong hands-on expertise with Playwright (TypeScript/JavaScript). · Experience building custom automation frameworks and utilities from scratch. · Proficiency in testing AI/ML-integrated applications: inference endpoints, personalization engines, chatbots, or predictive dashboards. · Solid knowledge of HTTP protocols, API testing (Postman, Supertest, RestAssured). · Familiarity with MLOps and model lifecycle management (e.g., via MLflow, SageMaker, Vertex AI). · Experience in testing data pipelines (ETL, streaming, batch), synthetic data generation, and test data versioning. Domain Knowledge: · Exposure to NLP, CV, recommendation engines, time-series forecasting, or tabular ML models. · Understanding of key ML metrics (precision, recall, F1-score, AUC), model drift, and concept drift. · Knowledge of bias/fairness auditing, especially in UI/UX contexts where AI decisions are shown to users. Leadership & Communication: · Proven experience leading QA/Automation teams (4+ engineers). · Strong documentation, code review, and stakeholder communication skills. · Experience collaborating in Agile/SAFe environments with cross-functional teams. --- Preferred Qualifications: · Experience with AI Explainability frameworks like LIME, SHAP, or What-If Tool. · Familiarity with Test Data Management platforms (e.g., Tonic.ai, Delphix) for ML training/inference data. · Background in performance and load testing for AI systems using tools like Locust, JMeter, or k6. · Experience with GraphQL, Kafka, or event-driven architecture testing. · QA Certifications (ISTQB, Certified Selenium Engineer) or cloud certifications (AWS, GCP, Azure). --- Education: · Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related technical discipline. · Bonus for certifications or formal training in Machine Learning, Data Science, or MLOps. --- Why Join Us? · Work on cutting-edge AI platforms shaping the future of [industry/domain]. · Collaborate with world-class AI researchers and engineers. · Drive the quality of products used by [millions of users / high-impact clients]. · Opportunity to define test automation practices for AI—one of the most exciting frontiers in tech.

Job Title: Generative AI Engineer Job Overview: We are on the lookout for a creative and technically proficient Generative AI Engineer to join our team. This individual will be responsible for architecting and deploying innovative AI systems centered around large language models (LLMs). The role emphasizes hands-on development in areas like prompt engineering, model customization, and real-world implementation of generative AI capabilities. As part of the team, you will help design and build applications such as AI chat interfaces, intelligent content generators, summarization engines, and other tools that utilize state-of-the-art LLM technologies. Key Responsibilities: Build, train, and implement generative AI systems using models such as GPT, BERT, LLaMA, Claude, or similar. Adapt and fine-tune pre-trained foundation models to meet specific business needs and domains. Integrate generative AI into various products, including conversational agents, summarization tools, and AI content creators. Conduct exploratory research to remain updated on the latest advancements in LLMs, prompt optimization, and multi-modal AI technologies. Prepare and manage relevant datasets for model training, evaluation, and testing. Collaborate with teams from data science, engineering, and product development to ensure smooth integration of AI models into live systems. Monitor model behavior continuously and make improvements to ensure high accuracy, low latency, and consistent reliability. Apply responsible AI principles and set up feedback mechanisms to refine model output over time. Required Skills and Qualifications: A Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related technical discipline. Proven experience in applying LLMs and generative AI techniques in production environments. Strong programming skills in Python and familiarity with machine learning libraries such as PyTorch, TensorFlow, and Hugging Face Transformers. Solid grounding in NLP methods, including text generation, translation, summarization, and conversational AI. Hands-on experience with techniques like prompt tuning, LoRA, PEFT, and RLHF for fine-tuning models. Understanding of cloud-based deployment using platforms such as AWS, GCP, or Azure. Strong analytical and debugging skills, with attention to performance tuning and cost optimization. Knowledge of MLOps best practices, including model version control (e.g., MLflow, Git), and container technologies like Docker and Kubernetes is advantageous.

Company Brief House of Shipping provides business consultancy and advisory services for Shipping & Logistics companies. House of Shipping's commitment to their customers begins with developing an understanding of their business fundamentals. We are hiring on behalf of one of our key US based client - a globally recognized service provider of flexible and scalable outsourced warehousing solutions, designed to adapt to the evolving demands of today’s supply chains. Currently House of Shipping is looking to identify a high caliber Data Science Lead . This position is an on-site position for Hyderabad . Background and experience: 15–18 years in data science, with 5+ years in leadership roles Proven track record in building and scaling data science teams in logistics, e-commerce, or manufacturing Strong understanding of statistical learning, ML architecture, productionizing models, and impact tracking Job purpose: To lead enterprise-scale data science initiatives in supply chain optimization, forecasting, network analytics, and predictive maintenance. This role blends technical leadership with strategic alignment across business units and manages advanced analytics teams to deliver measurable business impact. Main tasks and responsibilities: Define and drive the data science roadmap across forecasting (demand, returns), route optimization, warehouse simulation, inventory management, and fraud detection Architect end-to-end pipelines with engineering teams: from data ingestion, model development, to API deployment Lead the design and deployment of ML models using Python (Scikit-Learn, XGBoost, PyTorch, LightGBM), and MLOps tools like MLflow, Vertex AI, or AWS SageMaker Collaborate with operations, product, and technology to prioritize AI use cases and define business metrics Manage experimentation frameworks (A/B testing, simulation models) and statistical hypothesis testing Mentor team members in model explainability, interpretability, and ethical AI practices Ensure robust model validation, drift monitoring, retraining schedules, and version control Contribute to organizational data maturity: feature stores, reusable components, metadata tracking Own team hiring, capability development, project estimation, and stakeholder presentations Collaborate with external vendors, universities, and open-source projects where applicable Education requirements: Bachelor’s or Master’s or PhD in Computer Science, Mathematics, Statistics, Operations Research Preferred: Certifications in Cloud ML stacks (AWS/GCP/Azure), MLOps, or Applied AI Competencies and skills: Strategic vision in AI applications across supply chain Team mentorship and delivery ownership Expertise in statistical and ML frameworks MLOps pipeline management and deployment best practices Strong business alignment and executive communication

This is an WFO opportunity. Please do NOT apply if you are looking for a hybrid or WFH model. This is a Noida-based job. Please do NOT apply unless you are already in the NCR or actively looking to relocate to the NCR. We need a minimum of 2 years of experience. Do NOT apply unless you have a minimum of 2 years of hands-on experience in the job described in the JD below. Position : NLP & Generative AI Engineer Location : Noida Department : AI/ML Employment Type : Full-time About Gigaforce Gigaforce is a California-based InsurTech company delivering a next-generation, SaaS-based claims platform purpose-built for the Property and Casualty industry. Our blockchain-optimized solution integrates artificial intelligence (AI)-powered predictive models with deep domain expertise to streamline and accelerate subrogation and claims processing. Whether for insurers, recovery vendors, or other ecosystem participants, Gigaforce transforms the traditionally fragmented claims lifecycle into an intelligent, end-to-end digital experience. Recognized as one of the most promising emerging players in the insurance technology space, Gigaforce has already achieved significant milestones. We were a finalist for InsurtechNY, a leading platform accelerating innovation in the insurance industry, and twice named a Top 50 company by the TiE Silicon Valley community. Additionally, Plug and Play Tech Center, the worlds largest early-stage investor and innovation accelerator, selected Gigaforce to join its prestigious global accelerator headquartered in Sunnyvale, California. At the core of our platform is a commitment to cutting-edge innovation. We harness the power of technologies such as AI, Machine Learning, Robotic Process Automation, Blockchain, Big Data, and Cloud Computingleveraging modern languages and frameworks like Java, Kotlin, Angular, and Node.js. We are driven by a culture of curiosity, excellence, and inclusion. At Gigaforce, we hire top talent and provide an environment where every voice matters and every idea is valued. Our employees enjoy comprehensive medical benefits, equity participation, meal cards and generous paid time off. As an equal opportunity employer, we are proud to foster a diverse, equitable, and inclusive workplace that empowers all team members to thrive. Were seeking a NLP & Generative AI Engineers with 2-8 years of hands-on experience in traditional machine learning, natural language processing, and modern generative AI techniques. If you have experience deploying GenAI solutions to production, working with open-source technologies, and handling document-centric pipelines, this is the role for you. Youll work in a high-impact role, leading the design, development, and deployment of innovative AI/ML solutions for insurance claims processing and beyond. In this agile environment, you'll work within structured sprints and leverage data-driven insights and user feedback to guide decision-making. You'll balance strategic vision with tactical execution to ensure we continue to lead the industry in subrogation automation and claims optimization for the property and casualty insurance market. Key Responsibilities Build and deploy end-to-end NLP and GenAI-driven products focused on document understanding, summarization, classification, and retrieval. Design and implement models leveraging LLMs (e.g., GPT, T5, BERT) with capabilities like fine-tuning, instruction tuning, and prompt engineering. Work on scalable, cloud-based pipelines for training, serving, and monitoring models. Handle unstructured data from insurance-related documents such as claims, legal texts, and contracts. Collaborate cross-functionally with data scientists, ML engineers, product managers, and developers. Utilize and contribute to open-source tools and frameworks in the ML ecosystem. Deploy production-ready solutions using MLOps practices : Docker, Kubernetes, Airflow, MLflow, etc. Work on distributed/cloud systems (AWS, GCP, or Azure) with GPU-accelerated workflows. Evaluate and experiment with open-source LLMs and embeddings models (e.g., LangChain, Haystack, LlamaIndex, HuggingFace). Champion best practices in model validation, reproducibility, and responsible AI. Required Skills & Qualifications 2-8 years of experience as a Data Scientist, NLP Engineer, or ML Engineer. Strong grasp of traditional ML algorithms (SVMs, gradient boosting, etc.) and NLP fundamentals (word embeddings, topic modeling, text classification). Proven expertise in modern NLP & GenAI models, including : Transformer architectures (e.g., BERT, GPT, T5) Generative tasks : summarization, QA, chatbots, etc. Fine-tuning & prompt engineering for LLMs Experience with cloud platforms (especially AWS SageMaker, GCP, or Azure ML). Strong coding skills in Python, with libraries like Hugging Face, PyTorch, TensorFlow, Scikit-learn. Experience with open-source frameworks (LangChain, LlamaIndex, Haystack) preferred. Experience in document processing pipelines and understanding structured/unstructured insurance documents is a big plus. Familiar with MLOps tools such as MLflow, DVC, FastAPI, Docker, KubeFlow, Airflow. Familiarity with distributed computing and large-scale data processing (Spark, Hadoop, Databricks). Preferred Qualifications Experience deploying GenAI models in production environments. Contributions to open-source projects in ML/NLP/LLM space. Background in insurance, legal, or financial domain involving text-heavy workflows. Strong understanding of data privacy, ethical AI, and responsible model usage. (ref:hirist.tech)

We are seeking a highly skilled AI/ML Engineer to join our team. As an AI/ML Engineer, you will be responsible for designing, implementing, and optimizing machine learning solutions, encompassing traditional models, deep learning architectures, and generative AI systems. Your role will involve collaborating with data engineers and cross-functional teams to create scalable, ethical, and high-performance AI/ML solutions that contribute to business growth. Your key responsibilities will include developing, implementing, and optimizing AI/ML models using both traditional machine learning and deep learning techniques. You will also design and deploy generative AI models for innovative business applications, in addition to working closely with data engineers to establish and maintain high-quality data pipelines and preprocessing workflows. Integrating responsible AI practices to ensure ethical, explainable, and unbiased model behavior will be a crucial aspect of your role. Furthermore, you will be expected to develop and maintain MLOps workflows to streamline training, deployment, monitoring, and continuous integration of ML models. Your expertise will be essential in optimizing large language models (LLMs) for efficient inference, memory usage, and performance. Collaboration with product managers, data scientists, and engineering teams to seamlessly integrate AI/ML into core business processes will also be part of your responsibilities. Rigorous testing, validation, and benchmarking of models to ensure accuracy, reliability, and robustness are essential aspects of this role. To be successful in this position, you must possess a strong foundation in machine learning, deep learning, and statistical modeling techniques. Hands-on experience with TensorFlow, PyTorch, scikit-learn, or similar ML frameworks is required. Proficiency in Python and ML engineering tools such as MLflow, Kubeflow, or SageMaker is also necessary. Experience in deploying generative AI solutions, understanding responsible AI concepts, solid experience with MLOps pipelines, and proficiency in optimizing transformer models or LLMs for production workloads are key qualifications for this role. Additionally, familiarity with cloud services (AWS, GCP, Azure), containerized deployments (Docker, Kubernetes), as well as excellent problem-solving and communication skills are essential. Ability to work collaboratively with cross-functional teams is also a crucial requirement. Preferred qualifications include experience with data versioning tools like DVC or LakeFS, exposure to vector databases and retrieval-augmented generation (RAG) pipelines, knowledge of prompt engineering, fine-tuning, and quantization techniques for LLMs, familiarity with Agile workflows and sprint-based delivery, and contributions to open-source AI/ML projects or published papers in conferences/journals. Join our team at Lucent Innovation, an India-based IT solutions provider, and enjoy a work environment that promotes work-life balance. With a focus on employee well-being, we offer 5-day workweeks, flexible working hours, and a range of indoor/outdoor activities, employee trips, and celebratory events throughout the year. At Lucent Innovation, we value our employees" growth and success, providing in-house training, as well as quarterly and yearly rewards and appreciation. Perks: - 5-day workweeks - Flexible working hours - No hidden policies - Friendly working environment - In-house training - Quarterly and yearly rewards & appreciation,

We are seeking a dynamic professional with strong experience in Databricks and Machine Learning to design and implement scalable data pipelines and ML solutions. The ideal candidate will work closely with data scientists, analysts, and business teams to deliver high-performance data products and predictive models. Key Responsibilities: Design, develop, and optimize data pipelines using Databricks , PySpark , and Delta Lake Build and deploy Machine Learning models at scale Perform data wrangling , feature engineering , and model tuning Collaborate with cross-functional teams for ML model integration and monitoring Implement MLflow for model versioning and tracking Ensure best practices in MLOps , code management, and automation Must-Have Skills: Hands-on experience with Databricks , Spark , and SQL Strong knowledge of ML algorithms , Python (Pandas, Scikit-learn), and model deployment Familiarity with cloud platforms (Azure / AWS / GCP) Experience with CI/CD pipelines and ML lifecycle management tools Good to Have: Exposure to data governance , monitoring tools , and performance optimization Knowledge of Docker/Kubernetes and REST API integration

Ready to shape the future of work At Genpact, we don&rsquot just adapt to change&mdashwe drive it. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos , our industry-first accelerator, is an example of how we&rsquore scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to , our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, tech-driven environment, love solving real-world problems, and want to be part of a team that&rsquos shaping the future, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions - we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation , our teams implement data, technology, and AI to create tomorrow, today. Get to know us at and on , , , and . Inviting applications for the role of Lead Consultant - ML/CV Ops Engineer ! We are seeking a highly skilled ML CV Ops Engineer to join our AI Engineering team. This role is focused on operationalizing Computer Vision models&mdashensuring they are efficiently trained, deployed, monitored , and retrained across scalable infrastructure or edge environments. The ideal candidate has deep technical knowledge of ML infrastructure, DevOps practices, and hands-on experience with CV pipelines in production. You&rsquoll work closely with data scientists, DevOps, and software engineers to ensure computer vision models are robust, secure, and production-ready always. Key Responsibilities: End-to-End Pipeline Automation: Build and maintain ML pipelines for computer vision tasks (data ingestion, preprocessing, model training, evaluation, inference). Use tools like MLflow , Kubeflow, DVC, and Airflow to automate workflows. Model Deployment & Serving: Package and deploy CV models using Docker and orchestration platforms like Kubernetes. Use model-serving frameworks (TensorFlow Serving, TorchServe , Triton Inference Server) to enable real-time and batch inference. Monitoring & Observability: Set up model monitoring to detect drift, latency spikes, and performance degradation. Integrate custom metrics and dashboards using Prometheus, Grafana, and similar tools. Model Optimization: Convert and optimize models using ONNX, TensorRT , or OpenVINO for performance and edge deployment. Implement quantization, pruning, and benchmarking pipelines. Edge AI Enablement (Optional but Valuable): Deploy models on edge devices (e.g., NVIDIA Jetson, Coral, Raspberry Pi) and manage updates and logs remotely. Collaboration & Support: Partner with Data Scientists to productionize experiments and guide model selection based on deployment constraints. Work with DevOps to integrate ML models into CI/CD pipelines and cloud-native architecture. Qualifications we seek in you! Minimum Qualifications Bachelor&rsquos or Master&rsquos in Computer Science , Engineering, or a related field. Sound experience in ML engineering, with significant work in computer vision and model operations. Strong coding skills in Python and familiarity with scripting for automation. Hands-on experience with PyTorch , TensorFlow, OpenCV, and model lifecycle tools like MLflow , DVC, or SageMaker. Solid understanding of containerization and orchestration (Docker, Kubernetes). Experience with cloud services (AWS/GCP/Azure) for model deployment and storage. Preferred Qualifications: Experience with real-time video analytics or image-based inference systems. Knowledge of MLOps best practices (model registries, lineage, versioning). Familiarity with edge AI deployment and acceleration toolkits (e.g., TensorRT , DeepStream ). Exposure to CI/CD pipelines and modern DevOps tooling (Jenkins, GitLab CI, ArgoCD ). Contributions to open-source ML/CV tooling or experience with labeling workflows (CVAT, Label Studio). Why join Genpact Be a transformation leader - Work at the cutting edge of AI, automation, and digital innovation Make an impact - Drive change for global enterprises and solve business challenges that matter Accelerate your career - Get hands-on experience, mentorship, and continuous learning opportunities Work with the best - Join 140,000+ bold thinkers and problem-solvers who push boundaries every day Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the tech shapers and growth makers at Genpact and take your career in the only direction that matters: Up. Let&rsquos build tomorrow together. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color , religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training.

Ready to shape the future of work At Genpact, we don&rsquot just adapt to change&mdashwe drive it. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos , our industry-first accelerator, is an example of how we&rsquore scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to , our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, tech-driven environment, love solving real-world problems, and want to be part of a team that&rsquos shaping the future, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions - we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation , our teams implement data, technology, and AI to create tomorrow, today. Get to know us at and on , , , and . Inviting applications for the role of Senior Principal Consultant - Data Scientists with Computer vision experience! We are seeking a tenured and highly skilled Data Scientist with deep expertise in Computer Vision and a strong foundation in AI/ML modeling. The ideal candidate will not only lead the development of intelligent vision systems but will also serve as a technical mentor, providing guidance to junior data scientists on model selection, optimization, and deployment strategies. Experience in domains such as energy, power generation, industrial equipment, or manufacturing will be considered a strong advantage, as the role involves solving real-world visual AI/ML problems in industrial environments. Key Responsibilities: Lead CV Projects: Design and deliver Computer Vision models across a range of use cases (e.g., anomaly detection, visual inspections, OCR, predictive maintenance). Model Development: Develop, evaluate, and optimize state-of-the-art AI/ML models (e.g., CNNs, Vision Transformers, YOLO, Faster R-CNN, etc.). Mentorship: Guide junior and mid-level data scientists on best practices in feature engineering, model selection, evaluation metrics, and problem-solving strategies. Domain Translation: Translate complex industrial problems into AI-driven CV solutions that can scale in production environments. Collaboration: Work closely with software engineers, MLOps , and business teams to ensure model integration and operational success. Code Quality & Experimentation: Drive code modularity, reproducibility, and experimentation through use of ML pipelines, version control, and testing. Innovation & Research: Stay current with latest CV and AI/ML advancements and apply them appropriately to business problems. Stakeholder Communication: Present insights, models, and outcomes in a clear and impactful way to both technical and non-technical stakeholders. Qualifications we seek in you! Minimum Qualifications Master&rsquos or PhD in Computer Science, Machine Learning, AI, Electrical Engineering, or a related field. industry experience in building and deploying machine learning models, with a strong portfolio in Computer Vision. Deep expertise in ML frameworks and CV libraries such as PyTorch , TensorFlow, OpenCV, Detectron2, MMDetection , etc. Solid understanding of core AI/ML algorithms - classification, regression, segmentation, object detection, time-series, clustering, etc. Experience with MLOps tools (e.g., MLflow , DVC, Kubeflow) and cloud platforms (AWS/GCP/Azure). Strong communication , leadership, and team collaboration skills . Preferred Qualifications: Prior experience in domains such as energy, utilities, power generation, or industrial systems is highly preferred. Experience deploying CV models within real-time environments. Contributions to open-source CV projects or published research. Proficient in statistical modelling, machine learning techniques, AI algorithms, and generative model development using large language models such as GPT-3, BERT, or similar frameworks like RAG, Knowledge Graphs etc. Lead the development of CI/CD pipelines and standardize deployment frameworks. Strong Python programming skills. Why join Genpact Be a transformation leader - Work at the cutting edge of AI, automation, and digital innovation Make an impact - Drive change for global enterprises and solve business challenges that matter Accelerate your career - Get hands-on experience, mentorship, and continuous learning opportunities Work with the best - Join 140,000+ bold thinkers and problem-solvers who push boundaries every day Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the tech shapers and growth makers at Genpact and take your career in the only direction that matters: Up. Let&rsquos build tomorrow together. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color , religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training.

Company Description About Sutherland Artificial Intelligence. Automation.Cloud engineering. Advanced analytics.For business leaders, these are key factors of success. For us, they’re our core expertise. We work with iconic brands worldwide. We bring them a unique value proposition through market-leading technology and business process excellence. We’ve created over 200 unique inventions under several patents across AI and other critical technologies. Leveraging our advanced products and platforms, we drive digital transformation, optimize critical business operations, reinvent experiences, and pioneer new solutions, all provided through a seamless “as a service” model. For each company, we provide new keys for their businesses, the people they work with, and the customers they serve. We tailor proven and rapid formulas, to fit their unique DNA.We bring together human expertise and artificial intelligence to develop digital chemistry. This unlocks new possibilities, transformative outcomes and enduring relationships. Sutherland Unlocking digital performance. Delivering measurable results. Job Description We are looking for a proactive and detail-oriented AI OPS Engineer to support the deployment, monitoring, and maintenance of AI/ML models in production. Reporting to the AI Developer, this role will focus on MLOps practices including model versioning, CI/CD, observability, and performance optimization in cloud and hybrid environments. Key Responsibilities: Build and manage CI/CD pipelines for ML models using platforms like MLflow, Kubeflow, or SageMaker. Monitor model performance and health using observability tools and dashboards. Ensure automated retraining, version control, rollback strategies, and audit logging for production models. Support deployment of LLMs, RAG pipelines, and agentic AI systems in scalable, containerized environments. Collaborate with AI Developers and Architects to ensure reliable and secure integration of models into enterprise systems. Troubleshoot runtime issues, latency, and accuracy drift in model predictions and APIs. Contribute to infrastructure automation using Terraform, Docker, Kubernetes, or similar technologies. Qualifications Required Qualifications: 3–5 years of experience in DevOps, MLOps, or platform engineering roles with exposure to AI/ML workflows. Hands-on experience with deployment tools like Jenkins, Argo, GitHub Actions, or Azure DevOps. Strong scripting skills (Python, Bash) and familiarity with cloud environments (AWS, Azure, GCP). Understanding of containerization, service orchestration, and monitoring tools (Prometheus, Grafana, ELK). Bachelor’s degree in computer science, IT, or a related field. Preferred Skills: Experience supporting GenAI or LLM applications in production. Familiarity with vector databases, model registries, and feature stores. Exposure to security and compliance standards in model lifecycle management

Role Summary: The AIML Platform Engineering Lead is a pivotal leadership role responsible for managing the day-to-day operations and development of the AI/ML platform team. In this role, you will guide the team in designing, building, and maintaining scalable platforms, while collaborating with other engineering and data science teams to ensure successful model deployment and lifecycle management. Key Responsibilities: Lead and manage a team of platform engineers in developing and maintaining robust AI/ML platforms. Define and implement best practices for machine learning infrastructure, ensuring scalability, performance, and security. Collaborate closely with data scientists and DevOps teams to optimize the ML lifecycle from model training to deployment. Establish and enforce standards for platform automation, monitoring, and operational efficiency. Serve as the primary liaison between engineering teams, product teams, and leadership. Mentor and develop junior engineers, providing technical guidance and performance feedback. Stay abreast of the latest advancements in AI/ML infrastructure and integrate new technologies where applicable. Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. 8+ years of experience in Python & Node.js development and infrastructure. Proven experience in leading engineering teams and driving large-scale projects. Extensive expertise in cloud infrastructure (AWS, GCP, Azure), MLOps tools (e.g., Kubeflow, MLflow), and infrastructure as code (Terraform) Strong programming skills in Python and Node.js, with a proven track record of building scalable and maintainable systems that support AI/ML workflows. Hands-on experience with monitoring and observability tools, such as Datadog, to ensure platform reliability and performance. Strong leadership and communication skills with the ability to influence cross-functional teams. Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment. Job Type: Full-time Benefits: Commuter assistance Flexible schedule Health insurance Life insurance Paid sick time Paid time off Provident Fund Work from home Ability to commute/relocate: Noida, Uttar Pradesh: Reliably commute or planning to relocate before starting work (Preferred) Application Question(s): What are your salary expectations? What is your notice period? Location: Noida, Uttar Pradesh (Preferred) Work Location: In person

WorkMode :Hybrid Work Location : Chennai / Hyderabad / Work Timing : 2 PM to 11 PM Primary : Data Scientist We are seeking a skilled Data Scientist with strong expertise in Python programming and Amazon SageMaker to join our data team. The ideal candidate will have a solid foundation in machine learning, data analysis, and cloud-based model deployment. You will work closely with cross-functional teams to build, deploy, and optimize predictive models and data-driven solutions at scale. Bachelors or Master's degree in Computer Science, Data Science, Statistics, or a related field. 12+ years of experience in data science or machine learning roles. Proficiency in Python and popular ML libraries (e.g., scikit-learn, pandas, NumPy). Hands-on experience with Amazon SageMaker for model training, tuning, and deployment. Strong understanding of supervised and unsupervised learning techniques. Experience working with large datasets and cloud platforms (AWS preferred). Excellent problem-solving and communication skills. Experience with AWS services beyond SageMaker (e.g., S3, Lambda, Step Functions). Familiarity with deep learning frameworks like TensorFlow or PyTorch. Exposure to MLOps practices and tools (e.g., CI/CD for ML, MLflow, Kubeflow). Knowledge of version control (e.g., Git) and agile development practices.

Role Overview: We are looking for a skilled and versatile AI Infrastructure Engineer (DevOps/MLOps) to build and manage the cloud infrastructure, deployment pipelines, and machine learning operations behind our AI-powered products. You will work at the intersection of software engineering, ML, and cloud architecture to ensure that our models and systems are scalable, reliable, and production-ready. Key Responsibilities: Design and manage CI/CD pipelines for both software applications and machine learning workflows. Deploy and monitor ML models in production using tools like MLflow, SageMaker, Vertex AI, or similar. Automate the provisioning and configuration of infrastructure using IaC tools (Terraform, Pulumi, etc.). Build robust monitoring, logging, and alerting systems for AI applications. Manage containerized services with Docker and orchestration platforms like Kubernetes. Collaborate with data scientists and ML engineers to streamline model experimentation, versioning, and deployment. Optimize compute resources and storage costs across cloud environments (AWS, GCP, or Azure). Ensure system reliability, scalability, and security across all environments. Requirements: 5+ years of experience in DevOps, MLOps, or infrastructure engineering roles. Hands-on experience with cloud platforms (AWS, GCP, or Azure) and services related to ML workloads. Strong knowledge of CI/CD tools (e.g., GitHub Actions, Jenkins, GitLab CI). Proficiency in Docker, Kubernetes, and infrastructure-as-code frameworks. Experience with ML pipelines, model versioning, and ML monitoring tools. Scripting skills in Python, Bash, or similar for automation tasks. Familiarity with monitoring/logging tools (Prometheus, Grafana, ELK, CloudWatch, etc.). Understanding of ML lifecycle management and reproducibility. Preferred Qualifications: Experience with Kubeflow, MLflow, DVC, or Triton Inference Server. Exposure to data versioning, feature stores, and model registries. Certification in AWS/GCP DevOps or Machine Learning Engineering is a plus. Background in software engineering, data engineering, or ML research is a bonus. What We Offer: Work on cutting-edge AI platforms and infrastructure Cross-functional collaboration with top ML, research, and product teams Competitive compensation package – no constraints for the right candidate send mail to :- thasleema@qcentro.com Job Type: Permanent Ability to commute/relocate: Thiruvananthapuram District, Kerala: Reliably commute or planning to relocate before starting work (Required) Experience: Devops and MLops: 5 years (Required) Work Location: In person

About this role: Wells Fargo is seeking a Senior Software Engineer. In this role, you will: Lead complex technology initiatives including those that are companywide with broad impact Act as a key participant in developing standards and companywide best practices for engineering complex and large-scale technology solutions for technology engineering disciplines Design, code, test, debug, and document for projects and programs Review and analyze complex, large-scale technology solutions for tactical and strategic business objectives, enterprise technological environment, and technical challenges that require in-depth evaluation of multiple factors, including intangibles or unprecedented technical factors Make decisions in developing standard and companywide best practices for engineering and technology solutions requiring understanding of industry best practices and new technologies, influencing and leading technology team to meet deliverables and drive new initiatives Collaborate and consult with key technical experts, senior technology team, and external industry groups to resolve complex technical issues and achieve goals Lead projects, teams, or serve as a peer mentor Required Qualifications: 5+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Desired Qualifications: Strong Python programming skills Expertise in RPA tools such as UI path Expertise in Workflow automation tools such as Power Platform Minimum 2 Years of hands-on experience in AI/ML and Gen AI Proven experience in LLMs (Gemini, GPT or Llama etc.) Extensive experience in Prompt Engineering and model fine tuning AI/Gen AI Certifications from premier institution Hands on experience in ML ops (MLflow, CICD pipelines) Job Expectations: Design and develop AI driven automation solutions Implement AI automation to enhance process automation Develop and maintain automation, BOTS and AI based workflows Integrate Ai automation with existing applications, API's and databases Design, develop and implement Gen AI applications using LLM's Build and optimize prompt engineering workflows Fine tune and integrate pre-trained models for specific use cases Deploy models in production using robust MLops practices Posting End Date: 23 Jul 2025 *Job posting may come down early due to volume of applicants. We Value Equal Opportunity Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic. Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit's risk appetite and all risk and compliance program requirements. Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process. Applicants with Disabilities To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo . Drug and Alcohol Policy Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more. Wells Fargo Recruitment and Hiring Requirements: a. Third-Party recordings are prohibited unless authorized by Wells Fargo. b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.

About this role: Wells Fargo is seeking a Senior Software Engineer. In this role, you will: Lead moderately complex initiatives and deliverables within technical domain environments Contribute to large scale planning of strategies Design, code, test, debug, and document for projects and programs associated with technology domain, including upgrades and deployments Review moderately complex technical challenges that require an in-depth evaluation of technologies and procedures Resolve moderately complex issues and lead a team to meet existing client needs or potential new clients' needs while leveraging solid understanding of the function, policies, procedures, or compliance requirements Collaborate and consult with peers, colleagues, and mid-level managers to resolve technical challenges and achieve goals Lead projects and act as an escalation point, provide guidance and direction to less experienced staff Required Qualifications: 4+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Desired Qualifications: Strong Python programming skills Expertise in RPA tools such as UI path Expertise in Workflow automation tools such as Power Platform Minimum 2 Years of hands-on experience in AI/ML and Gen AI Proven experience in LLMs (Gemini, GPT or Llama etc.) Extensive experience in Prompt Engineering and model fine tuning AI/Gen AI Certifications from premier institution Hands on experience in ML ops (MLflow, CICD pipelines) Job Expectations: Design and develop AI driven Automation solutions Implement AI automation to enhance process automation Develop and maintain automation, BOTS and AI based workflows Integrate Ai automation with existing applications, API's and databases Design, develop and implement Gen AI applications using LLM's Build and optimize prompt engineering workflows Fine tune and integrate pre-trained models for specific use cases Deploy models in production using robust MLops practices Posting End Date: 23 Jul 2025 *Job posting may come down early due to volume of applicants. We Value Equal Opportunity Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic. Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit's risk appetite and all risk and compliance program requirements. Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process. Applicants with Disabilities To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo . Drug and Alcohol Policy Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more. Wells Fargo Recruitment and Hiring Requirements: a. Third-Party recordings are prohibited unless authorized by Wells Fargo. b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.

Job Description Alimentation Couche-Tard Inc., (ACT) is a global Fortune 200 company and a leader in the convenience store and fuel space with over 16,700 stores. It has footprints across 31 countries and territories. Circle K India Data & Analytics team is an integral part of ACT’s Global Data & Analytics Team, and the Associate ML Ops Analyst will be a key player on this team that will help grow analytics globally at ACT. The hired candidate will partner with multiple departments, including Global Marketing, Merchandising, Global Technology, and Business Units. About the role The incumbent will be responsible for implementing Azure data services to deliver scalable and sustainable solutions, build model deployment and monitor pipelines to meet business needs. Roles & Responsibilities Development and Integration Collaborate with data scientists to deploy ML models into production environments Implement and maintain CI/CD pipelines for machine learning workflows Use version control tools (e.g., Git) and ML lifecycle management tools (e.g., MLflow) for model tracking, versioning, and management. Design, build as well as optimize applications containerization and orchestration with Docker and Kubernetes and cloud platforms like AWS or Azure Automation & Monitoring Automating pipelines using understanding of Apache Spark and ETL tools like Informatica PowerCenter, Informatica BDM or DEI, Stream Sets and Apache Airflow Implement model monitoring and alerting systems to track model performance, accuracy, and data drift in production environments. Collaboration and Communication Work closely with data scientists to ensure that models are production-ready Collaborate with Data Engineering and Tech teams to ensure infrastructure is optimized for scaling ML applications. Optimization and Scaling Optimize ML pipelines for performance and cost-effectiveness Operational Excellence Help the Data teams leverage best practices to implement Enterprise level solutions. Follow industry standards in coding solutions and follow programming life cycle to ensure standard practices across the project Helping to define common coding standards and model monitoring performance best practices Continuously evaluate the latest packages and frameworks in the ML ecosystem Build automated model deployment data engineering pipelines from plain Python/PySpark mode Stakeholder Engagement Collaborate with Data Scientists, Data Engineers, cloud platform and application engineers to create and implement cloud policies and governance for ML model life cycle. Job Requirements Education & Relevant Experience Bachelor’s degree required, preferably with a quantitative focus (Statistics, Business Analytics, Data Science, Math, Economics, etc.) Master’s degree preferred (MBA/MS Computer Science/M.Tech Computer Science, etc.) 1-2 years of relevant working experience in MLOps Behavioural Skills Delivery Excellence Business disposition Social intelligence Innovation and agility Knowledge Knowledge of core computer science concepts such as common data structures and algorithms, OOPs Programming languages (R, Python, PySpark, etc.) Big data technologies & framework (AWS, Azure, GCP, Hadoop, Spark, etc.) Enterprise reporting systems, relational (MySQL, Microsoft SQL Server etc.), non-relational (MongoDB, DynamoDB) database management systems and Data Engineering tools Exposure to ETL tools and version controlling Experience in building and maintaining CI/CD pipelines for ML models. Understanding of machine-learning, information retrieval or recommendation systems Familiarity with DevOps tools (Docker, Kubernetes, Jenkins, GitLab). #LI-DS1

Job Description Alimentation Couche-Tard Inc., (ACT) is a global Fortune 200 company. A leader in the convenience store and fuel space, it has footprint across 31 countries and territories. Circle K India Data & Analytics team is an integral part of ACT’s Global Data & Analytics Team, and the Data Scientist/Senior Data Scientist will be a key player on this team that will help grow analytics globally at ACT. The hired candidate will partner with multiple departments, including Global Marketing, Merchandising, Global Technology, and Business Units. ___________________________________________________________________________________________________________ Department: Data & Analytics Location: Cyber Hub, Gurugram, Haryana (5 days in office) Job Type: Permanent, Full-Time (40 Hours) Reports To: Senior Manager Data Science & Analytics ____________________________________________________________________________________________________________ About the role The incumbent will be responsible for delivering advanced analytics projects that drive business results including interpreting business, selecting the appropriate methodology, data cleaning, exploratory data analysis, model building, and creation of polished deliverables. Roles & Responsibilities Analytics & Strategy Analyse large-scale structured and unstructured data; develop deep-dive analyses and machine learning models in retail, marketing, merchandising, and other areas of the business Utilize data mining, statistical and machine learning techniques to derive business value from store, product, operations, financial, and customer transactional data Apply multiple algorithms or architectures and recommend the best model with in-depth description to evangelize data-driven business decisions Utilize cloud setup to extract processed data for statistical modelling and big data analysis, and visualization tools to represent large sets of time series/cross-sectional data Operational Excellence Follow industry standards in coding solutions and follow programming life cycle to ensure standard practices across the project Structure hypothesis, build thoughtful analyses, develop underlying data models and bring clarity to previously undefined problems Partner with Data Engineering to build, design and maintain core data infrastructure, pipelines and data workflows to automate dashboards and analyses. Stakeholder Engagement Working collaboratively across multiple sets of stakeholders – Business functions, Data Engineers, Data Visualization experts to deliver on project deliverables Articulate complex data science models to business teams and present the insights in easily understandable and innovative formats Job Requirements Education Bachelor’s degree required, preferably with a quantitative focus (Statistics, Business Analytics, Data Science, Math, Economics, etc.) Master’s degree preferred (MBA/MS Computer Science/M.Tech Computer Science, etc.) Relevant Experience 3 - 4 years for Data Scientist Relevant working experience in a data science/advanced analytics role Behavioural Skills Delivery Excellence Business disposition Social intelligence Innovation and agility Knowledge Functional Analytics (Supply chain analytics, Marketing Analytics, Customer Analytics, etc.) Statistical modelling using Analytical tools (R, Python, KNIME, etc.) Knowledge of statistics and experimental design (A/B testing, hypothesis testing, causal inference) Practical experience building scalable ML models, feature engineering, model evaluation metrics, and statistical inference. Practical experience deploying models using MLOps tools and practices (e.g., MLflow, DVC, Docker, etc.) Strong coding proficiency in Python (Pandas, Scikit-learn, PyTorch/TensorFlow, etc.) Big data technologies & framework (AWS, Azure, GCP, Hadoop, Spark, etc.) Enterprise reporting systems, relational (MySQL, Microsoft SQL Server etc.), non-relational (MongoDB, DynamoDB) database management systems and Data Engineering tools Business intelligence & reporting (Power BI, Tableau, Alteryx, etc.) Microsoft Office applications (MS Excel, etc.) #LI-DS1

About the Role We are seeking an experienced Senior DevOps/MLOps Engineer to lead and manage a high-performing engineering team. You will oversee the deployment and scaling of machine learning models and backend services using modern DevOps and MLOps practices. Proficiency in FastAPI , Docker , Kubernetes , and CI/CD is essential. Key Responsibilities Team Leadership : Guide and manage a team of DevOps/MLOps engineers. FastAPI Deployment : Optimize, containerize, and deploy FastAPI applications at scale. Infrastructure as Code (IaC) : Use tools like Terraform or Helm to manage infrastructure. Kubernetes Management : Handle multi-environment Kubernetes clusters (GKE, EKS, AKS, or on-prem). Model Ops : Manage ML model lifecycle: versioning, deployment, monitoring, and rollback. CI/CD Pipelines : Design and maintain robust pipelines for model and application deployment. Monitoring & Logging : Set up observability tools (Prometheus, Grafana, ELK, etc.). Security & Compliance : Ensure secure infrastructure and data pipelines. Required Skills FastAPI : Deep understanding of building, scaling, and securing APIs. Docker & Kubernetes : Expert-level experience in containerization and orchestration. CI/CD Tools : GitHub Actions, GitLab CI, Jenkins, ArgoCD, or similar. Cloud Platforms : AWS/GCP/Azure. Python : Strong scripting and automation skills. ML Workflow Tools (preferred): MLflow, DVC, Kubeflow, or Seldon. Preferred Qualifications Experience in managing hybrid cloud/on-premise deployments. Strong communication and mentoring skills. Understanding of data pipelines, feature stores, and model drift monitoring. Job Types: Full-time, Permanent Pay: ₹426,830.06 - ₹1,653,904.80 per year Work Location: In person Speak with the employer +91 9867786230

ROLES & RESPONSIBILITIES Qualifications and Skills: •Master’s or Ph.D. in Computer Science, Statistics, Mathematics, Data Science, or a related field. 5+ years of hands-on experience in data science or machine learning roles. Strong proficiency in Python or R, with deep knowledge of libraries like scikit-learn, pandas, NumPy, TensorFlow, or PyTorch. Proficient in SQL and working with relational databases. Solid experience with Azure cloud platforms and data pipeline tools. Strong grasp of statistical methods, machine learning algorithms, and model evaluation techniques. Excellent communication and storytelling skills with the ability to influence stakeholders. Proven track record of delivering impactful data science solutions in a business setting. Preferred Qualifications: Experience working in industries such as [logistics, aerospace, marketing, etc.]. Familiarity with MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow). Knowledge of data visualization tools (e.g., Tableau, Power BI, Plotly). Responsibilities and Duties •Model Development: Design, build, and deploy scalable machine learning models to solve key business challenges (e.g., customer churn, recommendation engines, pricing optimization). Data Analysis: Perform exploratory data analysis (EDA), statistical testing, and feature engineering to uncover trends and actionable insights. Project Leadership: Lead end-to-end data science projects, including problem definition, data acquisition, modeling, and presentation of results to stakeholders. Cross-functional Collaboration: Partner with engineering, product, marketing, and business teams to integrate models into products and processes. Mentorship: Guide and mentor junior data scientists and analysts, helping them grow technically and professionally. Innovation: Stay current with the latest data science techniques, tools, and best practices. Evaluate and incorporate new technologies when appropriate. Communication: Translate complex analyses and findings into clear, compelling narratives for non-technical stakeholders. EXPERIENCE 8-11 Years SKILLS Primary Skill: Data Science Sub Skill(s): Data Science Additional Skill(s): Data Science

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. We are looking for an enthusiastic and curious Junior Data Scientist to join the Cloud Nova team. This is an excellent opportunity for someone with 2-3 years of experience to work on exciting projects involving Generative AI (GenAI), Retrieval-Augmented Generation (RAG), and deep learning. You will support senior data scientists and engineers in building and deploying AI models that solve real-world problems. Primary Responsibilities: Assist in developing and testing GenAI models using tools like LangChain and Hugging Face Transformers Support the creation of RAG pipelines and embedding-based search systems Help prepare datasets and perform exploratory data analysis Contribute to model evaluation and performance tracking Collaborate with team members to integrate models into applications Stay updated on the latest trends in AI and deep learning Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so Required Qualifications: Bachelor’s degree in Computer Science, Data Science, or a related field 1+ years of experience in data science, machine learning, or AI projects (internships count) Basic understanding of NLP and deep learning concepts Willingness to learn and grow in a collaborative environment Technical Skills: Programming: Python, SQL AI/ML: PyTorch or TensorFlow, Scikit-learn, Hugging Face Transformers GenAI Tools: LangChain, LlamaIndex (basic familiarity preferred) Data Tools: Pandas, NumPy, Jupyter Notebooks Version Control: Git Preferred Qualifications: Experience with vector databases (e.g., FAISS) Familiarity with MLOps tools like MLflow or Docker Exposure to cloud-based model deployment Technical Skills: Cloud: Exposure to Azure or AWS At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone - of every race, gender, sexuality, age, location and income - deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. We are looking for a versatile AI/ML Engineer to join the Our team, contributing to the design and deployment of scalable AI solutions across the full stack. This role blends machine learning engineering with frontend/backend development and cloud native microservices. You’ll work closely with data scientists, MLOps engineers, and product teams to bring generative AI capabilities like RAG and LLM based systems into production. Primary Responsibility: Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so Required Qualifications: Bachelor’s or masters in computer science, Engineering, or related field. 5+ years of experience in AI/ML engineering, full stack development, or MLOps. Proven experience deploying AI models in production environments. Solid understanding of microservices architecture and cloud native development. Familiarity with Agile/Scrum methodologies Technical Skills: Languages & Frameworks: Python, JavaScript/TypeScript, SQL, Scala ML Tools: MLflow, TensorFlow, PyTorch, Scikit learn Frontend: React.js, Angular (preferred), HTML/CSS Backend: Node.js, Spring Boot, REST APIs Cloud: Azure (preferred), UAIS, AWS DevOps & MLOps: Git, Jenkins, Docker, Kubernetes, Azure DevOps Data Engineering: Apache Spark/Databricks, Kafka, ETL pipelines Monitoring: Prometheus, Grafana RAG/LLM: LangChain, LlamaIndex, embedding pipelines, prompt engineering Preferred Qualifications: Experience with Spark, Hadoop Familiarity with Maven, Spring, XML, Tomcat Proficiency in Unix shell scripting and SQL Server At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone - of every race, gender, sexuality, age, location and income - deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.

At Trackier, we're revolutionizing the way businesses measure and optimize their marketing performance. As a leading Marketing Analytics & Attribution platform, we empower advertisers, agencies, and ad networks with powerful, real-time insights to drive growth and maximize ROI. In today's complex digital landscape, understanding every touchpoint of the customer journey is paramount. That's where Trackier comes in. Our robust platform provides comprehensive tracking, detailed analytics, and precise attribution models, ensuring you have a clear picture of what's working and why. From performance marketing campaigns to influencer collaborations and beyond, we give you the tools to make data-driven decisions that propel your business forward. We're passionate about transparency, efficiency, and delivering measurable results. Our commitment to innovation means we're constantly evolving our platform to meet the dynamic needs of the industry, helping our clients achieve their growth ambitions with confidence. Position Summary: We are seeking a driven and analytically-minded AI/ML Engineer with 2-3 years of experience to join our growing team. In this role, you will play a crucial part in the end-to-end lifecycle of our AI solutions, from understanding and preparing complex datasets to developing, deploying, and optimizing robust machine learning models. You will leverage your strong data analysis skills to identify patterns, generate insights, and translate them into effective AI strategies that drive business value. You will be working with LLM integrations where necessary to generate insights from data and enhancing chatbots using RAG and similar tech. Key Responsibilities: Data Understanding & Preparation: Collaborate with data stakeholders to understand business problems and data sources. Perform data loading, cleaning, and preparation, including handling missing values, data type conversions, and ensuring data integrity for large datasets. Feature Engineering: Identify, extract, and transform relevant features from raw data to optimize model performance. Model Development: Design, develop, train, and evaluate machine learning models (including deep learning, natural language processing,etc., as relevant to our domain) for various applications. System Integration: Integrate AI models into existing production systems and applications, ensuring scalability and reliability.Performance Optimization: Continuously monitor, analyze, and improve the performance, accuracy, and efficiency of AI models in production. Insight Generation & Communication: Translate complex analytical findings and model outputs into clear, concise, and actionable business insights and recommendations for end users. Research & Innovation: Stay abreast of the latest advancements in AI/ML research and actively explore new technologies and methodologies to enhance our capabilities. Deployment & MLOps: Contribute to the development and implementation of MLOps practices, including model versioning, CI/CD for ML, and model monitoring. Collaboration: Work closely with cross-functional teams, including product managers, software engineers to define requirements and deliver high-quality AI solutions. Documentation: Create clear and comprehensive documentation for models, code, and processes. Requirements Experience: 2-3 years of professional experience as an AI Engineer, Machine Learning Engineer, or a similar role focused on building and deploying ML solutions. Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, or a related quantitative field. Programming: Strong proficiency in Python and experience with relevant AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn, Keras). Data Manipulation & Analysis: Demonstrated strong skills in data loading, cleaning, manipulation, and preparation using Pandas and NumPy. EDA & Visualization: Proven ability to conduct exploratory data analysis and create effective visualizations using libraries to communicate insights. ML Fundamentals: Solid understanding of machine learning principles, algorithms (e.g., supervised, unsupervised, reinforcement learning), and statistical modeling. Software Engineering: Strong software engineering fundamentals, including experience with version control (Git), testing, and code review practices. Problem Solving: Excellent analytical and problem-solving skills with a keen attention to detail and the ability to derive actionable insights from data. Communication: Strong written and verbal communication skills, with the ability to explain complex technical concepts and present data-driven recommendations to both technical and non-technical stakeholders. Preferred Qualifications : Experience with cloud platforms (AWS, Azure, GCP) and their AI/ML services. Familiarity with containerization technologies (Docker, Kubernetes). Experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, Sagemaker). Knowledge of distributed computing frameworks (e.g., Spark). Contribution to open-source projects or relevant publications. Experience with agile development methodologies. Benefits Medical Insurance. 5 days working culture. Best in industry salary structure. Sponsored trips.