39 Dvc Jobs

Your role: Do you have a curious mind, want to be involved in the latest technology trends and like to solve problems that have a meaningful benefit to hundreds of users across the bank Join our Tech Services- Group Chief Technology Office team and become a core contributor for the execution of the banks global AI Strategy, particularly to help the bank deploy AI models quickly and efficiently! We are looking for an experienced Data Engineer or ML Engineer to drive the delivery of an innovative ecosystem of tools and services. In this AI focused role, you will contribute to the development of a state-of-the-art Model Development, Deployment and Governance environment for classic, generative and agentic AI. Responsibilities may include: - Country lead of other Senior Engineers - Direct interaction with product owners and internal users to identify requirements, development of technical solutions and execution - Lead Development of an SDK (Software Development Kit) to automatically capture data product, dataset and AI / ML model metadata. Also, leverage LLMs to generate descriptive information about assets - Integration and publication of metadata into UBS's AI Use-case inventory, model artifact registry and Enterprise Data Mesh data product and dataset catalogue for discovery and regulatory compliance purposes - Design and implementation of services that seamlessly collects runtime evidence and operational information about a data product or model and publishes it to appropriate visualization tools - Creation of a collection of starters/templates that accelerate the creation of new data products by leveraging a collection of the latest tools and services and providing diverse and rich experiences to the Devpod ecosystem. - Design and implementation of data contract and fine-grained access mechanisms to enable data consumption on a "need to know" basis Your team: You will be part of the Data Mesh & AI Tooling team, which is a newly established function within Group Chief Technology Office. We provide solutions to help the firm embrace Artificial Intelligence and Machine Learning. We work with the divisions and functions of the firm to provide innovative solutions that integrate with their existing platforms to provide new and enhanced capabilities. One of our current aims is to help a data scientist get a model into production in an accelerated timeframe with the appropriate controls and security. We offer a number of key capabilities: data discovery that uses AI/ML to help users find data and obtain access a secure and controlled manner, an AI Inventory that describes the models that have been built to help users build their own use cases and validate them with Model Risk Management, a containerized model development environment for a user to experiment and produce their models and a streamlined MLOps process that helps them track their experiments and promote their models. Your expertise: - PHD or Masters degree in Computer Science or any related advanced quantitative discipline - 10+ years industry experience with Python / Pandas, SQL / Spark, Azure fundamentals / Kubernetes and Gitlab - Additional experience in data engineering frameworks (Databricks / Kedro / Flyte), ML frameworks (MLFlow / DVC) and Agentic Frameworks (Langchain, Langgraph, CrewAI) is a plus - Experience in leading and mentoring other Senior Engineers - Ability to produce secure and clean code that is stable, scalable, operational, and well-performing. Be up to date with the latest IT standards (security, best practices). Understanding the security principles in the banking systems is a plus - Ability to work independently, manage individual project priorities, deadlines and deliverables - Willingness to quickly learn and adopt various technologies - Excellent English language written and verbal communication skills About Us: UBS is the worlds largest and the only truly global wealth manager. We operate through four business divisions: Global Wealth Management, Personal & Corporate Banking, Asset Management and the Investment Bank. Our global reach and the breadth of our expertise set us apart from our competitors. We have a presence in all major financial centers in more than 50 countries. Join us: At UBS, we know that it's our people, with their diverse skills, experiences and backgrounds, who drive our ongoing success. Were dedicated to our craft and passionate about putting our people first, with new challenges, a supportive team, opportunities to grow and flexible working options when possible. Our inclusive culture brings out the best in our employees, wherever they are on their career journey. We also recognize that great work is never done alone. Thats why collaboration is at the heart of everything we do. Because together, were more than ourselves. Were committed to disability inclusion and if you need reasonable accommodation/adjustments throughout our recruitment process, you can always contact us. Disclaimer / Policy Statements: UBS is an Equal Opportunity Employer. We respect and seek to empower each individual and support the diverse cultures, perspectives, skills and experiences within our workforce.,

Job Title: Data Scientist Agentic AI & MLOps Location: Bangalore - Hybrid (3 days work from office, 2 days from home) About Us: Our client delivers next-generation security analytics and operations management. They secure organisations worldwide by staying ahead of cyber threats, leveraging AI-reinforced capabilities for unparalleled protection. Job Overview: Were seeking a Senior Data Scientist to architect agentic AI solutions and own the full ML lifecyclefrom proof-of-concept to production. Youll operationalise LLMs, build agentic workflows, implement MLOps best practices, and design multi-agent systems for cybersecurity tasks. Key Responsibilities: Operationalise large language models and agentic workflows (LangChain, LangGraph, LlamaIndex, Crew.AI) to automate security decision-making and threat response. Design, deploy, and maintain multi-agent AI systems for log analysis, anomaly detection, and incident response. Build proof-of-concept GenAI solutions and evolve them into production-ready components on AWS (Bedrock, SageMaker, Lambda, EKS/ECS) using reusable best practices. Implement CI/CD pipelines for model training, validation, and deployment with GitHub Actions, Jenkins, and AWS CodePipeline. Manage model versioning with MLflow and DVC, set up automated testing, rollback procedures, and retraining workflows. Automate cloud infrastructure provisioning with Terraform and develop REST APIs and microservices containerised with Docker and Kubernetes. Monitor models and infrastructure through CloudWatch, Prometheus, and Grafana; analyse performance and optimise for cost and SLA compliance. Collaborate with data scientists, application developers, and security analysts to integrate agentic AI into existing security workflows. Qualifications: Bachelors or Masters in Computer Science, Data Science, AI or related quantitative discipline. 4+ years of software development experience, including 3+ years building and deploying LLM-based/agentic AI architectures. In-depth knowledge of generative AI fundamentals (LLMs, embeddings, vector databases, prompt engineering, RAG). Hands-on experience with LangChain, LangGraph, LlamaIndex, Crew.AI or equivalent agentic frameworks. Strong proficiency in Python and production-grade coding for data pipelines and AI workflows. Deep MLOps knowledge: CI/CD for ML, model monitoring, automated retraining, and production-quality best practices. Extensive AWS experience with Bedrock, SageMaker, Lambda, EKS/ECS, S3 (Athena, Glue, Snowflake preferred). Infrastructure as Code skills with Terraform. Experience building REST APIs, microservices, and containerization with Docker and Kubernetes. Solid data science fundamentals: feature engineering, model evaluation, data ingestion. Understanding of cybersecurity principles, SIEM data, and incident response. Excellent communication skills for both technical and non-technical audiences. Preferred Qualifications: AWS certifications (Solutions Architect, Developer Associate). Experience with Model Context Protocol (MCP) and RAG integrations. Familiarity with workflow orchestration tools (Apache Airflow). Experience with time series analysis, anomaly detection, and machine learning. Show more Show less

As a Senior Data Scientist at our client's organization, your role will involve architecting agentic AI solutions and overseeing the entire ML lifecycle, from proof-of-concept to production. You will play a key role in operationalizing large language models, designing multi-agent AI systems for cybersecurity tasks, and implementing MLOps best practices. Key Responsibilities: - Operationalise large language models and agentic workflows (LangChain, LangGraph, LlamaIndex, Crew.AI) to automate security decision-making and threat response. - Design, deploy, and maintain multi-agent AI systems for log analysis, anomaly detection, and incident response. - Build proof-of-concept GenAI solutions and evolve them into production-ready components on AWS (Bedrock, SageMaker, Lambda, EKS/ECS) using reusable best practices. - Implement CI/CD pipelines for model training, validation, and deployment with GitHub Actions, Jenkins, and AWS CodePipeline. - Manage model versioning with MLflow and DVC, set up automated testing, rollback procedures, and retraining workflows. - Automate cloud infrastructure provisioning with Terraform and develop REST APIs and microservices containerized with Docker and Kubernetes. - Monitor models and infrastructure through CloudWatch, Prometheus, and Grafana; analyze performance and optimize for cost and SLA compliance. - Collaborate with data scientists, application developers, and security analysts to integrate agentic AI into existing security workflows. Qualifications: - Bachelors or Masters in Computer Science, Data Science, AI, or related quantitative discipline. - 4+ years of software development experience, including 3+ years building and deploying LLM-based/agentic AI architectures. - In-depth knowledge of generative AI fundamentals (LLMs, embeddings, vector databases, prompt engineering, RAG). - Hands-on experience with LangChain, LangGraph, LlamaIndex, Crew.AI, or equivalent agentic frameworks. - Strong proficiency in Python and production-grade coding for data pipelines and AI workflows. - Deep MLOps knowledge: CI/CD for ML, model monitoring, automated retraining, and production-quality best practices. - Extensive AWS experience with Bedrock, SageMaker, Lambda, EKS/ECS, S3 (Athena, Glue, Snowflake preferred). - Infrastructure as Code skills with Terraform. - Experience building REST APIs, microservices, and containerization with Docker and Kubernetes. - Solid data science fundamentals: feature engineering, model evaluation, data ingestion. - Understanding of cybersecurity principles, SIEM data, and incident response. - Excellent communication skills for both technical and non-technical audiences. Preferred Qualifications: - AWS certifications (Solutions Architect, Developer Associate). - Experience with Model Context Protocol (MCP) and RAG integrations. - Familiarity with workflow orchestration tools (Apache Airflow). - Experience with time series analysis, anomaly detection, and machine learning.,

Job Title: Data Science/MLOps Engineer- Retail/CPG Domain Location: India (Remote/Hybrid - [Insert City]) Experience Level: 3-5 Years Employment Type: Full-Time Job Summary We are seeking a highly skilled and motivated Data Science & MLOps Engineer to join our growing team. This role combines expertise in traditional machine learning, model deployment, and scalable MLOps infrastructure to drive business impact in the Retail and Consumer Packaged Goods (CPG) domain. The ideal candidate will have a strong background in building and operationalizing end-to-end ML pipelines and delivering production-grade ML solutions aligned with real-world use cases such as demand forecasting, pricing optimization, churn prediction, and personalization. Key Responsibilities Design and develop robust machine learning models for a variety of use cases including prediction, classification, segmentation, and time-series forecasting. Build, automate, and monitor ML pipelines using tools such as MLflow, Kubeflow, Airflow, Metaflow , or equivalent. Collaborate with data engineers and domain experts to transform business problems into scalable ML solutions. Deploy models into production environments using containerization (Docker) and orchestration frameworks (Kubernetes, AWS SageMaker, Azure ML, GCP Vertex AI). Set up and maintain model versioning, tracking, retraining workflows, and CI/CD pipelines for ML systems. Implement testing, monitoring, and performance tracking for deployed models to ensure reliability, fairness, and accuracy. Collaborate with DevOps and platform teams to improve infrastructure, scalability, and cost-efficiency of ML workloads. Stay updated on developments in MLOps, traditional ML algorithms, and best practices for production machine learning. Required Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field. 3+ years of experience in developing, deploying, and maintaining ML models in production environments. Strong programming skills in Python,SQL , with experience using libraries such as scikit-learn, pandas, NumPy, XGBoost, LightGBM, Prophet, or TensorFlow/PyTorch for traditional ML. Hands-on experience with ML workflow tools like MLflow, Kubeflow Pipelines, Airflow, or SageMaker Pipelines. Experience deploying ML models using Docker, Kubernetes , or cloud-native ML platforms (AWS, Azure, GCP). Good understanding of EDA , model evaluation techniques, feature engineering, and data preprocessing for structured and semi-structured data. Proven ability to align ML solutions with retail/CPG business requirements and deliver measurable outcomes. Preferred Qualifications Experience with time-series modeling , prophet , recommendation systems, or demand forecasting in retail or CPG. Familiarity with model drift detection, automated retraining , and data versioning using tools like DVC or LakeFS. Exposure to CI/CD practices for ML , including testing, rollback strategies, and reproducibility. Why Join Us Be at the forefront of innovation by applying GenAI to real-world retail/CPG challenges. Work in a dynamic, fast-paced, and intellectually stimulating environment. Collaborate with cross-functional teams across data science, engineering, and domain consulting. Enjoy flexibility through a hybrid/remote work model and opportunities for continuous learning.

The project involves the development of an AI-based door control system that utilizes computer vision and deep learning technologies to manage boardings and exits through real-time video analysis. This system aims to detect individuals and objects in doorways, replacing traditional sensors with a single AI-powered solution. The project includes the implementation of deep learning models for people and object recognition, movement analysis, real-time processing, decision-making, and logging. The system must process video data on the edge, requiring models optimized for embedded deployment while ensuring detection accuracy and speed. It should function reliably under varying lighting conditions and handle complex scenarios such as multiple passengers, carried objects, and potential obstructions while adhering to strict safety standards. Task Descriptions: Task 1: Data Analysis and Annotation - Categorize data for analysis - Annotate data for training and validation purposes Task 2: Model Evaluation and Performance Analysis - Conduct comprehensive testing to generate detailed daily performance reports - Analyze and document detection failures and performance issues - Develop recommendations for model improvements based on observed failure patterns Task 3: Model Enhancement and Optimization - Prepare data for model training - Develop and optimize deep learning models Task 4: Extended Vision Capabilities - Enhance the system's vision capabilities for improved performance Required Skillset: - Strong expertise in computer vision and deep learning, particularly with PyTorch / TensorFlow and object detection models - Experience in data annotation, preparation, and validation for computer vision applications - Proficiency in model optimization, testing, and deployment on edge devices - Familiarity with MLOps tools such as AWS Sage Maker, DVC, and version control systems Input: - Existing labeled dataset from current deployments - New video data from existing installations - Current model architecture and weights - System performance requirements and specifications,

As a member of our team, your primary responsibility will be the development and training of foundational models across various modalities. You will be involved in the end-to-end lifecycle management of foundational model development, starting from data curation to model deployment, by collaborating closely with core team members. Your role will also entail conducting research to enhance model accuracy and efficiency, as well as implementing state-of-the-art AI techniques in Text/Speech and language processing. Collaboration with cross-functional teams will be essential as you work towards building robust AI stacks and seamlessly integrating them into production pipelines. You will be expected to develop pipelines for debugging, CI/CD, and ensuring observability throughout the development process. Demonstrating your ability to lead projects and offer innovative solutions will be crucial, along with documenting technical processes, model architectures, and experimental results, while maintaining clear and organized code repositories. Ideally, you should hold a Bachelor's or Master's degree in a related field and possess 2 to 5 years of industry experience in applied AI/ML. Proficiency in Python programming is a must, along with familiarity with a selection of tools such as TensorFlow, PyTorch, HF Transformers, NeMo, SLURM, Ray, Pytorch DDP, Deepspeed, NCCL, Git, DVC, MLFlow, W&B, KubeFlow, Dask, Milvus, Apache Spark, Numpy, Whisper, Voicebox, VALL-E, HuBERT/Unitspeech, LLMOPs Tools, Dockers, DSPy, Langgraph, langchain, and llamaindex. If you are passionate about AI and eager to contribute to cutting-edge projects in the field, we welcome your application to join our dynamic team.,

The successful candidate will be instrumental in helping to build, test, and maintain the infrastructure and tools to allow for the speedy development and release of software and updates. You will also be responsible for pushing the limits of a wide breadth of the tools and approaches to AI/Machine Learning for advanced products and systems using video information and IoT systems. Your experience researching and developing new projects with a team will be crucial, as you apply your knowledge and communication abilities to discuss problem requirements and plan new ideas with the team and stakeholders. Your responsibilities will include building and setting up new development tools and infrastructure, designing, building, and maintaining efficient, reusable, and tested code in Python and other applicable languages and library tools. Understanding the needs of stakeholders and effectively conveying them to developers, working on ways to automate and improve development and release processes, and bringing your professional experience in working with video technologies will be part of your day-to-day tasks. You will deploy Machine Learning (ML) to large production environments, drive continuous learning in the AI and computer vision fields, test and examine code written by others, analyze results, ensure system safety and security against cybersecurity threats, and identify technical problems and develop software updates and fixes. Additionally, you will work closely with software developers and engineers to ensure that development follows established processes and works as intended, as well as plan out projects and be involved in project management decisions. To qualify for this role, you must have a minimum of 3 years of hands-on experience with AWS services and products (Batch, SageMaker, StepFunctions, CloudFormation/CDK), strong Python experience, and at least 3 years of experience with Machine Learning/AI or Computer Vision development/engineering. You should be able to provide technical leadership to developers for designing and securing solutions, have an understanding of Linux utilities and Bash, familiarity with containerization using Docker, and experience with data pipeline frameworks such as MetaFlow. Exposure to technologies/tools like Keras, Pandas, TensorFlow, PyTorch, Caffe, NumPy, and DVC/CML is preferred. Practical experience deploying Computer Vision/Machine Learning solutions at scale into production will be an advantage. At GlobalLogic, we prioritize a culture of caring, where we consistently put people first. You will experience an inclusive culture of acceptance and belonging, build meaningful connections with collaborative teammates, supportive managers, and compassionate leaders. We are committed to your continuous learning and development, offering numerous opportunities to try new things, sharpen your skills, and advance your career. With interesting and meaningful work on impactful projects, a high-trust organization, and a focus on balance and flexibility, GlobalLogic provides a supportive and enriching environment for your professional growth and personal well-being.,

The position is based at Altimetrik with base locations in Bangalore, Chennai, Pune, Jaipur, Hyderabad, Gurugram. The ideal candidate should be able to join immediately or within 10 days. As a Machine Learning Engineer, your primary responsibility will be to design and implement ML solutions while architecting scalable and efficient systems. You should be proficient in Machine Learning Algorithms, Data Engineering, ETL/ELT processes, data cleaning, preprocessing, EDA, feature engineering, data splitting, and encoding. Additionally, you should have experience in MLOps, including model versioning, training, experimenting, deployment, and monitoring using tools such as Python, Pandas, TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, LightGBM, Matplotlib, R, Scala, Java, Git, DVC, MLFlow, Kubernetes, Kubeflow, Docker, Containers, CI/CD deployments, Apache Airflow, Databricks, Snowflake, Salesforce, SAP, AWS/Azure/GCP Data Cloud Platforms, AWS SageMaker, Google AI Platform, Azure Machine Learning, model design and optimization, LLMs models (OpenAI, BERT, LLaMA, Gemini, etc.), RDBMS, NoSQL databases, Vector DB, RAG Pipelines, AI Agent Frameworks, AI agent authentication, deployment, AI security and compliance, and Prompt Engineering. Your secondary skills should include cloud computing, data engineering, and DevOps. You will be responsible for designing and developing AI/ML models and algorithms. Collaboration with data scientists and engineers to ensure the scalability and performance of AI/ML systems will be a key part of your role. To be considered for this position, you should have 12-15 years of experience in AI/ML development, strong expertise in AI/ML frameworks and tools, and excellent problem-solving and technical skills.,

We are currently organizing an in-person recruitment drive for individuals with Data Science expertise in Pune and Bangalore on 31st May 2025. We are in search of a skilled and experienced GenAI Developer to become part of our expanding data competency team. The ideal candidate should possess a solid background in working with GEN AI, ML, LangChain, LangGraph, GenAI Architecture Strategy, and Prompt engineering. Your main responsibilities will involve collaborating closely with our data analysts, engineers, and business units to ensure the optimal performance, scalability, and availability of our data pipelines and analytics. As a Data Science professional, you will be located in either Pune or Bangalore, depending on your preference. The desired experience for this role ranges from 4 to 12 years, and it is a full-time employment opportunity. Your primary duties will include seeking a Machine Learning Engineer proficient in Langchain, ML modeling, and MLOps to construct and implement production-ready AI systems. You will design and deploy ML models and pipelines, develop intelligent applications using Langchain & LLMs, and establish MLOps workflows for training, deployment, and monitoring to ensure model reproducibility and scalability. The expertise you are expected to bring to this position includes proficiency in Python, Scikit-learn, TensorFlow/PyTorch, Langchain, LLM integration, MLOps tools (MLflow, Kubeflow, DVC), Docker, Git, CI/CD, and Cloud platforms like AWS/GCP/Azure. In return for your contributions, we offer a competitive salary and benefits package, along with a culture that emphasizes talent development through quarterly promotion cycles, company-sponsored higher education, and certifications. You will have the opportunity to work with cutting-edge technologies and participate in employee engagement programs such as project parties, flexible work hours, Long Service awards, annual health check-ups, and comprehensive insurance coverage for yourself, spouse, children, and parents. At Persistent Ltd., we are committed to creating an inclusive work environment that celebrates diversity and fosters equal opportunities for all individuals. We encourage applications from qualified candidates with disabilities and from diverse backgrounds, irrespective of gender or gender preference. Our hybrid work options, flexible hours, and accessible facilities aim to accommodate various needs and preferences, ensuring a supportive workplace for all employees. If you have specific requirements due to disabilities, please inform us during the application process or at any time during your employment. We are dedicated to providing an inclusive environment where every employee can thrive and succeed. Persistent promotes a values-driven and people-centric work culture that enables employees to grow both professionally and personally, make a positive impact using the latest technologies, engage in collaborative innovation, and explore global opportunities to learn and work alongside industry experts. Join us at Persistent and unlock your full potential. We are an Equal Opportunity Employer that upholds a zero-tolerance policy against discrimination and harassment of any form.,

Ready to build the future with AI At Genpact, we don&rsquot just keep up with technology&mdashwe set the pace. 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, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what&rsquos possible, 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 Business Analyst , Data Scientist In this role, w e 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. 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 Lead AI-first transformation - Build and scale AI solutions that redefine industries Make an impact - Drive change for global enterprises and solve business challenges that matter Accelerate your career &mdashGain hands-on experience, world-class training, mentorship, and AI certifications to advance your skills Grow with the best - Learn from top engineers, data scientists, and AI experts in a dynamic, fast-moving workplace Committed to ethical AI - Work in an environment where governance, transparency, and security are at the core of everything we build 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 140,000+ coders, 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.

We are seeking an experienced and results-oriented AI Developer to join our team. In this role, you will be tasked with developing, deploying, and optimizing AI and machine learning models to address real-world business challenges. Collaboration with cross-functional teams will be essential to deliver scalable and ethical AI solutions utilizing cutting-edge tools and technologies. With a minimum of 2 years of experience, your key responsibilities will encompass various aspects: Data Engineering & Preprocessing - Collaborate with data scientists and engineers to acquire, clean, and preprocess extensive datasets. - Conduct feature engineering and data selection to enhance model inputs. AI Model Development & Implementation - Design, construct, and validate machine learning and deep learning models, including CNNs, RNNs/LSTMs, Transformers, NLP and computer vision models, reinforcement learning agents, and classical ML techniques. - Develop models tailored to specific business challenges within domains. Performance Optimization & Scalability - Enhance models for performance, latency, scalability, and resource efficiency. - Ensure models are ready for production in real-time applications. Deployment, MLOps & Integration - Establish and maintain MLOps pipelines for model deployment, monitoring, and retraining. - Utilize Docker, Kubernetes, and CI/CD tools for containerization and orchestration. - Deploy models on cloud platforms (AWS, Azure, GCP) or on-site infrastructure. - Integrate models into systems and applications via APIs or model-serving frameworks. Testing, Validation & Continuous Improvement - Implement testing strategies such as unit testing, regression testing, and A/B testing. - Continuously enhance models based on user feedback and performance metrics. Research & Innovation - Stay informed about AI/ML advancements, tools, and techniques. - Experiment with novel approaches to drive innovation and maintain a competitive edge. Collaboration & Communication - Collaborate closely with engineers, product managers, and subject matter experts. - Document model architecture, training processes, and experimental results. - Clearly communicate complex technical subjects to non-technical stakeholders. Ethical AI Practices - Support and implement ethical AI practices focusing on fairness, transparency, and accountability. Required Qualifications & Skills Core Technical Skills - Proficiency in Python and experience with libraries like TensorFlow, PyTorch, Keras, and Scikit-learn. - Solid grasp of ML/DL architectures including CNNs, RNNs/LSTMs, and Transformers. - Proficient in data manipulation using Pandas, NumPy, and SciPy. MLOps & Deployment Experience - Experience with MLOps tools such as MLflow, Kubeflow, and DVC. - Familiarity with Docker, Kubernetes, and CI/CD pipelines. - Demonstrated ability to deploy models on cloud platforms (AWS, Azure, or GCP). Software Engineering & Analytical Thinking - Strong foundation in software engineering principles: Git, unit testing, and code optimization. - Strong analytical mindset with experience handling large datasets. Communication & Teamwork - Excellent written and verbal communication skills. - Collaborative team player with prior experience in agile environments. Preferred Advanced AI & LLM Expertise - Hands-on experience with LLMs (e.g., GPT, Claude, Mistral, LLaMA). - Familiarity with prompt engineering and Retrieval-Augmented Generation (RAG). - Experience with LangChain, LlamaIndex, and Hugging Face Transformers. - Understanding of vector databases such as Pinecone, FAISS, and Weaviate. Domain-Specific Experience - Experience applying AI in sectors like healthcare, finance, retail, manufacturing, or customer service. - Specialized knowledge in NLP, computer vision, or reinforcement learning. Academic & Research Background - Strong background in statistics and optimization. - Research publications in top AI/ML conferences (e.g., NeurIPS, ICML, CVPR, ACL) are a plus.,

Role & responsibilities Design and build robust MLOps pipelines for model training, validation, deployment, and monitoring Automate workflows using CI/CD tools such as GitLab Actions, Azure DevOps, Jenkins, or Argo Workflows Build and manage ML workloads on AWS (SageMaker Unified studio, Bedrock, EKS, Lambda, S3, Athena) and Azure (Azure ML Foundry, AKS, ADF, Blob Storage) 70% azure 30% AWS/100% Azure Design secure and cost-efficient ML architecture leveraging cloud-native services Manage infrastructure using IaC tools such as Terraform, Bicep, or CloudFormation Implement cost optimization and performance tuning for cloud workloads Package ML models using Docker, and orchestrate deployments with Kubernetes on EKS/AKS Ensure robust CI/CD pipelines and infrastructure as code (IaC) using tools like Terraform or CloudFormation Integrate observability tools for model performance, drift detection, and lineage tracking (e.g., Fiddler, MLflow, Prometheus, Grafana, Azure Monitor, CloudWatch) Ensure model reproducibility, versioning, and compliance with audit and regulatory requirements Collaborate with data scientists, software engineers, DevOps, and cloud architects to operationalize AI/ML use cases Mentor junior MLOps engineers and evangelize MLOps best practices across teams LLM Ops knowledge in required Preferred candidate profile Bachelor's/Masters in Computer Science, Engineering, or related discipline 7-13 years in Devops, with 2-4+ years in MLOps or cloud ML engineering. Proficient with MLflow, DVC, Airflow, FastAPI, Docker, Kubernetes, and Git/Jenkins . Experience with feature stores (e.g., Feast), model registries, and experiment tracking Lead capabilities Devops & MLOps, Automation Cloud formation/Teraform/BICEP

We are seeking an experienced and results-driven AI Developer to become a valuable part of our team. Your main responsibility will involve developing, deploying, and optimizing AI and machine learning models to address real-world business challenges. Collaborating with diverse teams, you will work towards delivering scalable and ethical AI solutions utilizing cutting-edge tools and technologies. You will be involved in various aspects of AI model development and implementation, including data engineering, preprocessing, and feature selection to enhance model inputs. Your role will also focus on designing, building, and validating machine learning and deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs/LSTMs), Transformers, NLP, computer vision models, reinforcement learning agents, and classical ML techniques. Customizing models to meet specific business requirements will be a key part of your responsibilities. Optimizing model performance, latency, scalability, and resource efficiency, ensuring they are suitable for real-time applications, will be crucial. You will also be responsible for deploying models, managing MLOps pipelines, and integrating models into various systems and applications using APIs or model-serving frameworks. Implementing testing strategies, continuously improving models based on user feedback and performance metrics, and staying abreast of AI/ML advancements and techniques are essential aspects of this role. Collaborating closely with different stakeholders, documenting model architecture, and communicating complex technical topics to non-technical audiences will be part of your routine. In addition to technical skills such as proficiency in Python, experience with libraries like TensorFlow, PyTorch, Keras, Scikit-learn, ML/DL architectures, data manipulation using Pandas, NumPy, SciPy, MLOps tools, Docker, Kubernetes, and CI/CD pipelines, you are expected to possess strong software engineering, analytical, and communication skills. Experience in agile environments and a proven track record in deploying models on cloud platforms are also highly valued. Preferred qualifications include advanced expertise in AI and LLM, domain-specific experience in sectors like healthcare, finance, retail, manufacturing, or customer service, and academic/research background with a strong foundation in statistics and optimization. Hands-on experience with LLMs, familiarity with prompt engineering and RAG, and exposure to vector databases are considered advantageous. Research publications in top AI/ML conferences will be viewed as a significant asset.,

Ready to build the future with AI At Genpact, we don&rsquot just keep up with technology&mdashwe set the pace. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos AI Gigafactory, 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 agentic AI, our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what&rsquos possible, 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 genpact.com and on LinkedIn, X, YouTube, and Facebook. Inviting applications for the role Lead Consultant - Data Scientist - Generative AI of & Advanced Analytics Job Summary: We are looking for a highly motivated Data Scientist with experience in Generative AI (GenAI) to join our advanced analytics team. In this role, you will blend classical data science with cutting-edge GenAI techniques (LLMs, embeddings, RAG) to solve real-world business problems, build intelligent systems, and push the boundaries of AI adoption across domains. Key Responsibilities . Build and deploy Generative AI models using large language models (LLMs) like GPT,Gemini ,Claude, LLaMA, etc. . Design and develop Retrieval-Augmented Generation (RAG) pipelines for enterprise search and summarization. . Perform exploratory data analysis, feature engineering, and apply statistical methods to derive insights. . Work with vector databases (e.g., FAISS, Pinecone, Chroma) and embedding models for semantic search. . Develop and fine-tune prompts for prompt engineering, few-shot learning, and function calling. . Collaborate with product and engineering teams to design AI-driven features and solutions. . Conduct A/B testing, build dashboards, and contribute to ML pipelines in production. . Stay updated with GenAI research and open-source tools evaluate new models and frameworks. . solving high-impact problems involving structured and unstructured data using a wide range of ML/DL techniques. . The ideal candidate is hands-on, self-driven, and passionate about turning data into intelligent systems. . Strong Capabilities on SQL, Python, Vscode/pytorch and have come across using Tensorflow, Keras Qualifications we seek in you! Minimum Qualifications Required: . Bachelor%27s or master&rsquos degree in data science, Computer Science, Statistics, or a related field. . experience in data science, machine learning, or applied AI roles. . Hands-on experience with GenAI platforms (OpenAI, Anthropic, Hugging Face, Cohere, etc.). . Strong Python skills: pandas, scikit-learn, transformers, LangChain, etc. . Experience working with LLMs, embeddings, or chatbot development. . Familiarity with MLOps, model versioning, and deployment in cloud environments (AWS/GCP/Azure). . Experience with RAG architecture, vector databases, and knowledge graphs. . Prior work in text summarization, document intelligence, or multi-modal AI. . Understanding of LLM fine-tuning, PEFT/LoRA, and instruction-tuned models. . Publications, open-source contributions, or GenAI product demos are a strong plus. . Strong in Python, with experience in NumPy, pandas, scikit-learn. . Proficiency in TensorFlow, PyTorch, or JAX. . Familiarity with deep learning concepts: backpropagation, activation functions, loss optimization. . Experience with cloud platforms: AWS, GCP, or Azure. . Comfortable with version control, CI/CD pipelines, and containerized environments (e.g., Docker). Tools & Stack You&rsquoll Work With . LLMs: GPT-4, Gemini . Libraries: Hugging Face Transformers, LangChain, OpenAI API . Vector Stores: FAISS, Pinecone, Weaviate . ML Libraries: Scikit-learn, MLflow, DVC, PyTorch, TensorFlow, XGBoost, scikit-learn . Storage: PostgreSQL, S3, BigQuery . Deployment: Docker, FastAPI, Airflow, Kubernetes . Cloud: AWS/GCP/Azure Why join Genpact . Lead AI-first transformation - Build and scale AI solutions that redefine industries . Make an impact - Drive change for global enterprises and solve business challenges that matter . Accelerate your career&mdashGain hands-on experience, world-class training, mentorship, and AI certifications to advance your skills . Grow with the best - Learn from top engineers, data scientists, and AI experts in a dynamic, fast-moving workplace . Committed to ethical AI - Work in an environment where governance, transparency, and security are at the core of everything we build . 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 140,000+ coders, 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 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.

???? Join The Family Office as an Associate AI Engineer Are you a recent graduate in Computer Science, Software Engineering, or Data Science, ready to turn your passion for AI into real-world impact Were looking for an Associate AI Engineer to join our team at The Family Office (TFO) . This role offers hands-on experience with cutting-edge AI projects, mentorship from experienced engineers, and the opportunity to shape the future of intelligent systems in enterprise environments ???? Why Join TFO At The Family Office, we live and work by our core values: Trust Transparency Honesty Commitment Youll be part of a forward-thinking, collaborative team solving real business problems using advanced AI tools such as Azure OpenAI, MLOps pipelines, and real-time observability platforms ???? What Youll Do ???? AI Model Development Fine-tune LLMs and build prompt-driven solutions using Azure OpenAI. Develop data preprocessing pipelines (cleaning, tokenization, normalization). Contribute to Retrieval-Augmented Generation (RAG) and Document Intelligence implementations. ???? MLOps & Lifecycle Management Implement Azure ML Pipelines, MLflow, and DVC for version control and tracking. Monitor model performance using Azure Monitor and Application Insights. Maintain AI notebooks and experiments through Azure AI Studio (Foundry). ???? Collaboration & Learning Work closely with Senior AI Engineers, Data Scientists, DataOps, and PlatformOps. Participate in peer reviews, documentation, and continuous learning. ? Quality & Delivery Ensure code quality, documentation standards, and timely delivery. Contribute to testing, validation, and integration of AI solutions into production. ???? Who Were Looking For Recent graduates in Computer Science, AI, ML, or Data Science. Hands-on experience in AI/ML projects. Fast learners with a strong desire to grow in a dynamic, supportive environment. Bonus: Experience with Azure AI services or certifications like Azure AI Fundamentals. ????? Tools & Technologies Languages: Python, SQL, JSON Frameworks: PyTorch, Hugging Face Transformers MLOps: Azure ML, Azure AI Studio, MLflow, DVC DataOps: Azure Data Factory, Synapse, Data Lake Monitoring: Azure Monitor, Application Insights AI: Prompt engineering, fine-tuning, OpenAI APIs, Azure OpenAI ???? Ready to Launch Your AI Career This isnt just a jobits your launchpad into the future of enterprise AI. If you&aposre curious, driven, and eager to build real-world solutions with impact, wed love to hear from you. ???? Apply today and take the first step toward shaping the future with AI. The Family Office is an independent wealth management firm offering customized investment solutions in alternative asset classes, including private equity, private debt, and real estate. Serving high-net-worth individuals and families, we provide tailored strategies to address unique financial needs with a focus on transparency, diversification, and long-term value. With a commitment to excellence and decades of expertise, The Family Office helps clients preserve and grow their wealth across generations. Show more Show less

You are a resilient, forward-thinking Python Developer with expertise in AI and Machine Learning deployment, interested in joining our growing technology team as a Python with AI/ML Developer. Your role will involve building, deploying, and maintaining scalable ML models and backend systems to support AI-driven products. To excel in this position, you must have a passion for innovation, attention to detail, and the ability to solve complex problems with effective technical solutions. Your responsibilities will include developing, testing, and deploying machine learning models using Python and AI frameworks such as TensorFlow, PyTorch, and scikit-learn. You will collaborate with data scientists, product teams, and engineers to transition prototypes into production-ready systems. Additionally, you will build and manage APIs and backend services using frameworks like Flask and FastAPI, deploy ML models in cloud or on-prem environments using Docker, Kubernetes, and CI/CD pipelines, and optimize model performance post-deployment. As a successful candidate, you should hold a Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related field and have at least 2 years of experience in Python development with a focus on AI/ML projects. You must possess strong knowledge of ML/AI tools and frameworks, experience deploying and maintaining ML models in production environments, proficiency in containerization and orchestration tools, and familiarity with REST APIs and microservice architecture. Knowledge of cloud platforms, problem-solving skills, and a proactive, team-oriented mindset are essential for this role. Preferred skills include experience with data streaming platforms, familiarity with MLOps tools, exposure to computer vision, NLP, or deep learning projects, knowledge of database systems, experience with interactive dashboard tools, and contributions to open-source AI/ML projects or Kaggle competitions. Staying current with the latest research and best practices in AI, ML, and MLOps, as well as documenting technical architecture and best practices are crucial aspects of this role. Troubleshooting and resolving issues related to ML model performance, deployment, and data pipelines will also be part of your responsibilities.,

We are looking for a skilled and enthusiastic Applied AI/ML Engineer to be a part of our team. As an Applied AI/ML Engineer, you will be responsible for leading the entire process of foundational model development, focusing on cutting-edge generative AI techniques. Your main objective will be to implement efficient learning methods for data and compute, specifically addressing challenges relevant to the Indian scenario. Your tasks will involve optimizing model training and inference pipelines, deploying production-ready models, ensuring scalability through distributed systems, and fine-tuning models for domain adaptation. Collaboration with various teams will be essential as you work towards building strong AI stacks and seamlessly integrating them into production pipelines. Apart from conducting research and experiments, you will be crucial in converting advanced models into operational systems that generate tangible results. Your leadership in this field will involve working closely with technical team members and subject matter experts, documenting technical processes, and maintaining well-structured codebases to encourage innovation and reproducibility. This position is perfect for proactive individuals who are passionate about spearheading significant advancements in generative AI and implementing scalable solutions for real-world impact. Your responsibilities will include: - Developing and training foundational models across different modalities - Managing the end-to-end lifecycle of foundational model development, from data curation to model deployment, through collaboration with core team members - Conducting research to enhance model accuracy and efficiency - Applying state-of-the-art AI techniques in Text/Speech and language processing - Collaborating with cross-functional teams to construct robust AI stacks and smoothly integrate them into production pipelines - Creating pipelines for debugging, CI/CD, and observability of the development process - Demonstrating project leadership and offering innovative solutions - Documenting technical processes, model architectures, and experimental outcomes, while maintaining clear and organized code repositories To be eligible for this role, you should hold a Bachelor's or Master's degree in a related field and possess 2 to 5 years of industry experience in applied AI/ML. Minimum requirements for this position include proficiency in Python programming and familiarity with 3-4 tools from the specified list below: - Foundational model libraries and frameworks (TensorFlow, PyTorch, HF Transformers, NeMo, etc) - Experience with distributed training (SLURM, Ray, Pytorch DDP, Deepspeed, NCCL, etc) - Inference servers (vLLM) - Version control systems and observability (Git, DVC, MLFlow, W&B, KubeFlow) - Data analysis and curation tools (Dask, Milvus, Apache Spark, Numpy) - Text-to-Speech tools (Whisper, Voicebox, VALL-E (X), HuBERT/Unitspeech) - LLMOPs Tools, Dockers, etc - Hands-on experience with AI application libraries and frameworks (DSPy, Langgraph, langchain, llamaindex, etc),

As an AI / ML Engineer, you will be responsible for utilizing your expertise in the field of Artificial Intelligence and Machine Learning to develop innovative solutions. You should hold a Bachelor's or Master's degree in Computer Science, Engineering, Data Science, AI/ML, Mathematics, or a related field. With a minimum of 6 years of experience in AI/ML, you are expected to demonstrate proficiency in Python and various ML libraries such as scikit-learn, XGBoost, pandas, NumPy, matplotlib, and seaborn. In this role, you will need a strong understanding of machine learning algorithms and deep learning architectures including CNNs, RNNs, and Transformers. Hands-on experience with TensorFlow, PyTorch, or Keras is essential. You should also have expertise in data preprocessing, feature selection, exploratory data analysis (EDA), and model interpretability. Additionally, familiarity with API development and deploying models using frameworks like Flask, FastAPI, or similar tools is required. Experience with MLOps tools such as MLflow, Kubeflow, DVC, and Airflow will be beneficial. Knowledge of cloud platforms like AWS (SageMaker, S3, Lambda), GCP (Vertex AI), or Azure ML is preferred. Proficiency in version control using Git, CI/CD processes, and containerization with Docker is essential for this role. Bonus skills that would be advantageous include familiarity with NLP frameworks (e.g., spaCy, NLTK, Hugging Face Transformers), Computer Vision experience using OpenCV or YOLO/Detectron, and knowledge of Reinforcement Learning or Generative AI (GANs, LLMs). Experience with vector databases such as Pinecone or Weaviate, as well as LangChain for AI agent building, is a plus. Familiarity with data labeling platforms and annotation workflows will also be beneficial. In addition to technical skills, you should possess soft skills such as an analytical mindset, strong problem-solving abilities, effective communication, and collaboration skills. The ability to work independently in a fast-paced, agile environment is crucial. A passion for AI/ML and a proactive approach to staying updated with the latest developments in the field are highly desirable for this role.,

???? Were Hiring: AI Engineer ???? Location: Bengaluru (Regular office-based role) ???? Employment Type: Full-Time | 5 Days a Week from Office ???? Qualification: B.E / B.Tech or equivalent degree in Computer Science, IT, or related field Are you passionate about building and scaling production-grade AI/ML systems We&aposre looking for a skilled AI Engineer to join our team in Bengaluru and help drive real-world impact through cutting-edge machine learning and GenAI technologies. ???? Must-Have Skills: 48 years of hands-on experience in designing, building, and deploying production-grade AI/ML solutions Proficiency in Python, PySpark, SQL , and ML libraries like Scikit-learn, XGBoost, LightGBM Cloud-native ML development experience on AWS (SageMaker), GCP (Vertex AI), or Azure ML Strong background in NLP/GenAI frameworks: Hugging Face, LangChain, LlamaIndex Practical knowledge of MLOps tools (MLflow, Weights & Biases, DVC) and deployment frameworks ( FastAPI, Flask, Docker, Kubernetes ) Excellent communication, stakeholder engagement, and team collaboration skills Good-to-Have: Publications, blog posts, or open-source contributions in AI/ML Experience leading AI strategy or owning technical roadmaps Show more Show less

About the Role: We are seeking an experienced MLOps Engineer to lead the deployment, scaling, and performance optimization of open-source Generative AI models on cloud infrastructure. Youll work at the intersection of machine learning, DevOps, and cloud engineering to help productize and operationalize large-scale LLM and diffusion models. Key Responsibilities: Design and implement scalable deployment pipelines for open-source Gen AI models (LLMs, diffusion models, etc.). Fine-tune and optimize models using techniques like LoRA, quantization, distillation, etc. Manage inference workloads, latency optimization, and GPU utilization. Build CI/CD pipelines for model training, validation, and deployment. Integrate observability, logging, and alerting for model and infrastructure monitoring. Automate resource provisioning using Terraform, Helm, or similar tools on GCP/AWS/Azure. Ensure model versioning, reproducibility, and rollback using tools like MLflow, DVC, or Weights & Biases. Collaborate with data scientists, backend engineers, and DevOps teams to ensure smooth production rollouts. Required Skills & Qualifications: 5+ years of total experience in software engineering or cloud infrastructure. 3+ years in MLOps with direct experience in deploying large Gen AI models. Hands-on experience with open-source models (e.g., LLaMA, Mistral, Stable Diffusion, Falcon, etc.). Strong knowledge of Docker, Kubernetes, and cloud compute orchestration. Proficiency in Python and familiarity with model-serving frameworks (e.g., FastAPI, Triton Inference Server, Hugging Face Accelerate, vLLM). Experience with cloud platforms (GCP preferred, AWS or Azure acceptable). Familiarity with distributed training, checkpointing, and model parallelism. Good to Have: Experience with low-latency inference systems and token streaming architectures. Familiarity with cost optimization and scaling strategies for GPU-based workloads. Exposure to LLMOps tools (LangChain, BentoML, Ray Serve, etc.). Why Join Us: Opportunity to work on cutting-edge Gen AI applications across industries. Collaborative team with deep expertise in AI, cloud, and enterprise software. Flexible work environment with a focus on innovation and impact. Show more Show less

You are an experienced Senior DevOps/MLOps Engineer who will be responsible for leading and managing a high-performing engineering team. Your main focus will be on overseeing the deployment and scaling of machine learning models and backend services using modern DevOps and MLOps practices. It is essential that you have proficiency in FastAPI, Docker, Kubernetes, and CI/CD. Your key responsibilities will include guiding and managing a team of DevOps/MLOps engineers, optimizing, containerizing, and deploying FastAPI applications at scale, managing infrastructure using tools like Terraform or Helm, handling multi-environment Kubernetes clusters (GKE, EKS, AKS, or on-prem), managing ML model lifecycle including versioning, deployment, monitoring, and rollback, designing and maintaining robust CI/CD pipelines for model and application deployment, setting up observability tools such as Prometheus, Grafana, ELK, ensuring secure infrastructure and data pipelines. Your required skills should include a deep understanding of building, scaling, and securing APIs with FastAPI, expert-level experience in Docker and Kubernetes for containerization and orchestration, familiarity with CI/CD tools like GitHub Actions, GitLab CI, Jenkins, ArgoCD, or similar, experience with cloud platforms such as AWS/GCP/Azure, strong scripting and automation skills with Python, and knowledge of ML Workflow Tools like MLflow, DVC, Kubeflow, or Seldon. Preferred qualifications for this role include experience in managing hybrid cloud/on-premise deployments, strong communication and mentoring skills, and an understanding of data pipelines, feature stores, and model drift monitoring. This is a full-time, permanent position that requires in-person work location.,

computer vision or deep learning roles industrial/safety inspection datasets (e.g., PPE detection, visual defect classification). Familiarity with MLOps tools like MLflow, DVC, or ClearML. ONNX, TensorRT, OpenVINO

We are seeking a skilled and driven AI Developer to create, implement, and launch advanced machine learning models and artificial intelligence solutions. The ideal candidate will possess strong programming abilities, a profound comprehension of machine learning algorithms, and proficiency in model deployment and refinement. Your primary responsibilities will include crafting and executing ML models and AI solutions utilizing suitable algorithms, examining extensive datasets to extract valuable patterns for business decisions, and refining machine learning models to ensure optimal performance. You will also be involved in constructing pipelines for data preprocessing, model training, testing, and deployment, as well as integrating ML models into production through REST APIs, Docker, or cloud services (e.g., AWS SageMaker, GCP AI Platform, Azure ML). Monitoring and enhancing model accuracy, scalability, and dependability post-deployment will also be part of your duties. Collaboration with cross-functional teams comprising product, engineering, and data science professionals to incorporate ML solutions into existing systems is crucial. You will be expected to research and implement new AI methodologies while staying abreast of the latest trends in the field. The ideal candidate should hold a Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Proficiency in Python and libraries like TensorFlow, PyTorch, and Scikit-learn is essential. A solid grasp of supervised, unsupervised, and reinforcement learning techniques is required, along with experience in NLP, computer vision, or time-series forecasting projects. Familiarity with data processing tools such as Pandas, NumPy, and SQL is expected, as well as knowledge of statistics, linear algebra, and probability theory. Experience in deploying models using Flask/FastAPI, Docker, and CI/CD pipelines is preferred. Familiarity with cloud platforms like AWS, GCP, or Azure, as well as proficiency in version control systems (Git) and collaborative tools like JIRA and Confluence, are advantageous. Hands-on experience with MLOps practices, model monitoring tools, Hugging Face Transformers, OpenCV, YOLO, and similar tools will be beneficial. Participation in Kaggle competitions, published papers, or GitHub AI/ML projects is considered a strong advantage. This is a full-time position located in Nagpur with a salary as per market standards. The ideal candidate should have 2-4 years of experience and be below 35 years old. Educational requirements include a Bachelors or Masters degree or equivalent in a relevant field. Proficiency in English and Hindi is required. Please note that there are no specific perks and benefits mentioned for this position.,

As a high-impact AI/ML Engineer, you will lead the design, development, and deployment of machine learning and AI solutions across vision, audio, and language modalities. You will be an integral part of a fast-paced, outcome-oriented AI & Analytics team, collaborating with data scientists, engineers, and product leaders to translate business use cases into real-time, scalable AI systems. Your responsibilities in this role will include architecting, developing, and deploying ML models for multimodal problems encompassing vision, audio, and NLP tasks. You will be responsible for the complete ML lifecycle, from data ingestion to model development, experimentation, evaluation, deployment, and monitoring. Leveraging transfer learning and self-supervised approaches where appropriate, you will design and implement scalable training pipelines and inference APIs using frameworks like PyTorch or TensorFlow. Collaborating with MLOps, data engineering, and DevOps teams, you will operationalize models using technologies such as Docker, Kubernetes, or serverless infrastructure. Continuously monitoring model performance and implementing retraining workflows to ensure sustained accuracy over time will be a key aspect of your role. You will stay informed about cutting-edge AI research and incorporate innovations such as generative AI, video understanding, and audio embeddings into production systems. Writing clean, well-documented, and reusable code to support agile experimentation and long-term platform development is an essential part of this position. To qualify for this role, you should hold a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field, with a minimum of 5-8 years of experience in AI/ML Engineering, including at least 3 years in applied deep learning. In terms of technical skills, you should be proficient in Python, with knowledge of R or Java being a plus. Additionally, you should have expertise in ML/DL Frameworks like PyTorch, TensorFlow, and Scikit-learn, as well as experience in Computer Vision tasks such as image classification, object detection, OCR, segmentation, and tracking. Familiarity with Audio AI tasks like speech recognition, sound classification, and audio embedding models is also desirable. Strong capabilities in Data Engineering using tools like Pandas, NumPy, SQL, and preprocessing pipelines for structured and unstructured data are required. Knowledge of NLP/LLMs, Cloud & MLOps services, deployment & infrastructure technologies, and CI/CD & Version Control tools are also beneficial. Soft skills and competencies that will be valuable in this role include strong analytical and systems thinking, effective communication skills to convey models and results to non-technical stakeholders, the ability to work cross-functionally with various teams, and a demonstrated bias for action, rapid experimentation, and iterative delivery of impact.,