281 Kubeflow Jobs

As a Lead Software Engineer Machine Learning, your role will involve driving the design, development, and deployment of advanced machine learning solutions. You will need to showcase strong leadership skills, deep technical expertise, and the ability to guide teams in solving complex, large-scale problems using cutting-edge ML technologies. Your responsibilities will include defining and leading the strategy and roadmap for ML systems and applications, architecting scalable machine learning systems, and collaborating with cross-functional teams to identify ML use cases and requirements. Mentoring junior engineers, monitoring and improving the performance of machine learning systems, and leading research initiatives for emerging ML techniques will also be part of your role. **Key Responsibilities:** - Define and lead the strategy and roadmap for ML systems and applications. - Architect and oversee the development of scalable machine learning systems and infrastructure. - Drive the design and implementation of advanced ML models and algorithms. - Collaborate with cross-functional teams to identify ML use cases and requirements. - Mentor and guide junior engineers in best practices for ML development. - Monitor, evaluate, and improve the performance of machine learning systems. - Lead research initiatives to explore emerging ML techniques. **Qualifications Required:** - Bachelors or Masters degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. A Ph.D. is a plus. - 10+ years of experience in software engineering, with at least 5 years focusing on machine learning. - Proficiency in ML frameworks and libraries like TensorFlow, PyTorch, and scikit-learn. - Strong expertise in designing and building large-scale, distributed ML systems. - Advanced knowledge of data engineering tools and frameworks, such as Spark, Hadoop, or Kafka. - Hands-on experience with cloud platforms (AWS, GCP, Azure) for ML workloads. - Expertise in deploying and managing ML models in production environments using MLOps tools like MLflow or Kubeflow. - Deep understanding of algorithms, data structures, and system design. - Experience with containerization (Docker) and orchestration (Kubernetes). The company is looking for a Lead Software Engineer Machine Learning with 10+ years of experience in data engineering or related fields. Strong leadership, problem-solving, and analytical thinking skills, along with excellent communication abilities, are essential for this role. You should also have the ability to foster collaboration, drive innovation, and effectively convey technical concepts to both technical and non-technical audiences.,

As an AI Engineer at American Airlines, you will be part of a diverse, high-performing team dedicated to technical excellence. Your focus will be on delivering unrivaled digital products that enhance the reliability and profitability of the airline. You will have the opportunity to work in the Machine Learning domain, applying algorithms and models to enable computers to learn from data and make predictions. This includes working on subfields such as segmentation, propensity modeling, natural language processing (NLP), reinforcement learning, and generative AI models. Key Responsibilities: - Collaborate with various stakeholders including leaders, business analysts, project managers, IT architects, and engineers to understand requirements and develop AI solutions based on business needs - Maintain and enhance existing enterprise services, applications, and platforms using domain-driven design and test-driven development - Troubleshoot and debug complex issues, identifying and implementing solutions - Research and implement new AI technologies to improve processes, security, and performance - Work closely with data scientists and product teams to build and deploy AI solutions, focusing on technical aspects of deployment and associated resources - Implement and optimize Python-based ML pipelines for data preprocessing, model training, and deployment - Monitor model performance, implement bias mitigation strategies, and ensure scalability and efficiency in production environments - Write and maintain code for model training and deployment, collaborating with software engineers to integrate models into applications - Partner with a diverse team to leverage cutting-edge technologies for building impactful AI solutions Qualifications Required: - Bachelor's degree in Computer Science, Computer Engineering, Data Science, Information Systems (CIS/MIS), Engineering, or related technical discipline, or equivalent experience/training - 3+ years of experience in designing, developing, and implementing large-scale machine learning applications in hosted production environments - Proficiency in Python, Databricks, Azure AI Foundry services, relational databases, cloud-based development, web servers, web services, AI/ML frameworks, MLOps tools, build/deployment tools, and object-oriented design principles - Strong understanding of governance frameworks for responsible AI, AI regulations, post-deployment model maintenance, and GEN AI & NLP concepts - Experience with VectorDatabases, data pipelines, and generative-based AI solutions using Large-Language Models (LLMs) - Proficiency in Microsoft Office Tools, Agile methodologies, and DevOps Toolchain practices - Excellent communication skills to interact effectively with stakeholders at all levels within the organization At American Airlines, you will have the opportunity to contribute to the largest airline in the world and make a difference in caring for people on life's journey. Feel free to bring your unique skills and personality to our team and be part of our mission.,

We are seeking skilled Machine Learning Engineers to design, develop, and deploy advanced ML models focused on Agentic AI use cases, utilizing the AWS ecosystem. The role emphasizes end-to-end ML solutions, from data preprocessing and model development to deployment and ongoing monitoring, with a strong focus on scalability, performance, and cost-efficiency in production environments. Key Responsibilities: Design, develop, and deploy machine learning models tailored for Agentic AI applications in clinical and enterprise domains. Work extensively with the AWS AI/ML ecosystem, including SageMaker, Bedrock, Lambda, Step Functions, S3, DynamoDB, and Kinesis, to build scalable solutions. Perform data preprocessing and feature engineering on structured, unstructured, and streaming data to build high-quality training datasets. Collaborate with Data Engineering teams to ensure robust, well-curated datasets while maintaining PHI/PII safety and compliance. Implement fine-tuning of large language models (LLMs), embeddings, and retrieval-augmented generation (RAG) pipelines. Evaluate and optimize models focusing on accuracy, performance, scalability, and operational cost-effectiveness. Integrate ML models into production applications and expose them via APIs for seamless consumption. Work closely with MLOps teams to automate workflows for model training, testing, deployment, and continuous monitoring. Conduct experimentation, A/B testing, and rigorous model validation to guarantee performance and reliability. Document experiments, data pipelines, model architectures, and best practices to ensure reproducibility and knowledge sharing. Required Skills & Qualifications: 3+ years of hands-on experience in machine learning engineering, model development, and production deployment. Strong proficiency in Python, with expertise in libraries such as NumPy, Pandas, Scikit-learn, PyTorch, and TensorFlow. Solid understanding of the full ML lifecycle, including data preprocessing, model training, evaluation, deployment, and monitoring. Experience working with AWS services for machine learning: SageMaker, Lambda, ECS/EKS, Step Functions, Bedrock, S3, and DynamoDB. Practical knowledge of large language models (LLMs), natural language processing (NLP), and vector embeddings. Proficient in developing APIs and deploying ML models as microservices in production environments. Experience in orchestrating ML pipelines using tools such as Airflow, Kubeflow, or MLflow. Familiarity with data versioning, experiment tracking, and model registry best practices. Strong SQL and NoSQL database skills, including experience with vector databases like Weaviate, Pinecone, or FAISS. Preferred Attributes: Experience in healthcare or regulated domains, ensuring compliance with industry standards. Excellent problem-solving skills, with a passion for experimentation, iteration, and delivering innovative AI solutions. Strong communication skills, capable of collaborating with cross-functional teams. Self-driven, with a focus on automating and improving ML workflows and processes.

Strong background in GPU architecture (NVIDIA, AMD) and HPC systems. Proficiency in AI/ML frameworks (TensorFlow, PyTorch, Keras, MXNet, Hugging Face).Experience with distributed training and orchestration frameworks (KubeFlow, MLflow, Ray, Horovod). Required Candidate profile Knowledge of parallel computing, MPI, CUDA, ROCm, and GPU drivers.Familiarity with storage technologies for HPC/AI (NVMe, Lustre, GPFS, Ceph, Object Storage).

• Proficiency with Python (Pandas, NumPy), SQL & Java • Experience with LLMs, Lang Chain, & Generative AI technologies • Familiarity with ML frameworks (TensorFlow, PyTorch) & data engineering tools (Spark, Kafka) • Build ML, NLP & recommender models Required Candidate profile • Understanding of key data engineering concepts like data lakes, columnar formats, ETL tools, & BI tools • ML, NLP, Recommender system, personalization, Segmentation, microservice architecture & API

You will be responsible for designing data pipelines and engineering infrastructure to support clients" enterprise machine learning systems at scale. Additionally, you will be tasked with taking offline models data scientists build and transforming them into real machine learning production systems. Your role will involve developing and deploying scalable tools and services for clients to handle machine learning training and inference efficiently. You will need to identify and evaluate new technologies to enhance the performance, maintainability, and reliability of clients" machine learning systems. It is essential to apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc. Furthermore, you will support model development with a focus on auditability, versioning, and data security. Your responsibilities will also include facilitating the development and deployment of proof-of-concept machine learning systems and communicating with clients to gather requirements and track progress. Qualifications: - Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent) - Strong software engineering skills in complex, multi-language systems - Fluency in Python - Comfort with Linux administration - Experience working with cloud computing and database systems - Experience building custom integrations between cloud-based systems using APIs - Experience developing and maintaining ML systems built with open source tools - Experience developing with containers and Kubernetes in cloud computing environments - Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.) - Ability to translate business needs to technical requirements - Strong understanding of software testing, benchmarking, and continuous integration - Exposure to machine learning methodology and best practices - Exposure to deep learning approaches and modeling frameworks (PyTorch, TensorFlow, Keras, etc.) Total Experience: 4+ years Education Qualification: BE/B.Tech/MCA/M.Sc/M.Tech,

As a Data Scientist at Texas Instruments, you will be a key player in the Demand Analytics team, focusing on shaping and executing demand planning and inventory buffer strategies. Working alongside a team of technical professionals, including application developers, system architects, data scientists, and data engineers, you will be responsible for solving complex business problems through innovative solutions that drive tangible business value. Your role will involve portfolio management for demand forecasting algorithms, generation of inventory buffer targets, segmentation of products, simulation/validation frameworks, and ensuring security and interoperability between capabilities. Key Responsibilities: - Engage strategically with stakeholder groups to align with TI's business strategy and goals - Communicate complex technical concepts effectively to influence final business outcomes - Collaborate with cross-functional teams to identify and prioritize actionable insights - Build scalable and modular technology stacks using modern technologies - Conduct simulations with various models to determine the best fit of algorithms - Research, experiment, and implement new approaches and models in line with business strategy - Lead data acquisition and engineering efforts - Develop and apply machine learning, AI, and data engineering frameworks - Write and debug code for complex development projects - Evaluate and determine the best modeling techniques for different scenarios Qualifications: Minimum requirements: - MS or PhD in a quantitative field or equivalent practical experience - 8+ years of professional experience in data science or related roles - 5+ years of hands-on experience developing and deploying time series forecasting models - Deep understanding of supply chain concepts like demand forecasting and inventory management - Proficiency in Python and core data science libraries - Experience taking machine learning models from prototype to production Preferred qualifications: - Experience with MLOps tools and platforms - Practical experience with cloud data science platforms - Familiarity with advanced forecasting techniques and NLP - Strong SQL skills and experience with large-scale data warehousing solutions About Texas Instruments: Texas Instruments is a global semiconductor company that designs, manufactures, and sells analog and embedded processing chips for various markets. At TI, we are passionate about creating a better world by making electronics more affordable through semiconductors. Our commitment to innovation drives us to build technology that is reliable, affordable, and energy-efficient, enabling semiconductors to be used in electronics everywhere. Join TI to engineer your future and collaborate with some of the brightest minds to shape the future of electronics. Embrace diversity and inclusion at TI, where every voice is valued and contributes to our strength as an organization. If you are ready to make an impact in the field of data science, apply to join our team at Texas Instruments.,

Seeking an MLOps Engineer to design, deploy, and monitor ML systems. You’ll ensure models are reliable, scalable, and easy to manage, while building tools that support teams and improve workflows. Required Candidate profile Looking for 3+ yrs exp in DevOps/MLOps/ML/Data Eng, strong Python, Git, CI/CD, Docker, K8s, cloud (AWS/GCP/Azure).Plus MLflow, Kubeflow, Airflow, PySpark; bonus Kafka, ArgoCD, Helm, Java, GPU.

Key Responsibilities Model Development & Optimization Lead the independent design, training, and validation of highly robust, reusable AI/ML models, leveraging and defining best practices for frameworks like Kubeflow, PyTorch, TensorFlow, or HuggingFace. Champion and apply advanced techniques in deep learning, NLP, computer vision, and classical ML, ensuring models are inherently explainable, scalable, and highly performant. Define strategies for and oversee the optimization of models for accuracy, speed, and resource efficiency, providing technical leadership and guidance to ML Data Engineers on deployment readiness. AI/ML System Design Architect complex and resilient AI model systems, effectively translating intricate business use cases into scalable architectures, including defining advanced pre-processing strategies and innovative feature engineering approaches. Drive the architectural vision, ensuring deep alignment between data characteristics, model architecture, and expected system behavior in large-scale production environments. Lead the collaboration with ML Data Engineers to establish and standardize robust integration patterns for embedding AI models into critical downstream workflows. Documentation & Standards Establish, enforce, and maintain comprehensive documentation standards for model assumptions, architecture choices, rigorous evaluation criteria, and recommended usage across the team. Lead the standardization of model development guidelines within the AI team, promoting best practices for reproducibility, traceability, and maintainability across the organization. Stakeholder Collaboration & Delivery Drive the end-to-end scoping, planning, and delivery of major AI features and strategic enhancements within broader data product initiatives, ensuring alignment with business objectives. Serve as a primary technical liaison and trusted advisor, proactively partnering with domain experts, business stakeholders, and functional analysts to iterate on model design, interpret complex results, and influence strategic decisions. Proactively identify opportunities for adaptation and enhancement based on feedback and performance monitoring, ensuring maximum realization of business value. Innovation & Capability Building Spearhead research, evaluation, and adoption of cutting-edge AI/ML methods, tools, and frameworks, assessing their potential to significantly improve our modeling practices and drive competitive advantage. Lead knowledge-sharing initiatives, design and facilitate advanced team learning sessions, establish playbooks, and drive prototyping initiatives to continuously advance the team&aposs and organization&aposs capabilities. Security & Compliance in AI Champion and ensure the rigorous integration of data privacy, access control, and advanced bias mitigation considerations throughout the model design and development lifecycle. Establish and enforce adherence to complex compliance, governance, and interpretability standards relevant to the business context, acting as a subject matter expert. Mentoring & Community Provide advanced technical mentorship, guidance, and code reviews to junior and mid-level AI engineers, fostering their growth in modeling approaches, model evaluation, and development best practices. Actively foster and lead a vibrant knowledge-sharing culture across the AI, data science, and engineering teams, contributing significantly to technical communities through presentations, workshops, and thought leadership. Required Qualifications Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 8+ years of extensive experience designing, architecting, and implementing highly impactful AI/ML models in production environments. Mastery of Python development skills, with expert-level familiarity and practical application of ML/DL libraries (e.g., Kubeflow, PyTorch, TensorFlow, scikit-learn) Proven track record of architecting and delivering highly impactful real-world AI solutions across complex use cases, including multi-agent systems, computer vision, natural language processing, and forecasting. Expert-level understanding and practical application of the full model lifecycle, including advanced MLOps practices, versioning, monitoring, and deployment strategies, often setting best practices for others. Exceptional leadership, collaboration, and communication skills, with the ability to articulate complex technical concepts to diverse technical and non-technical audiences, influence strategic decisions, and drive consensus across cross-functional teams. Preferred Skills Deep expertise with transformers, advanced embedding techniques, image recognition, or reinforcement learning. Extensive experience with Docker and designing secure deployments via APIs for seamless inference integration. Advanced understanding of data governance and AI ethics principles, with a focus on practical implementation. Ability to effectively lead and collaborate across highly distributed teams (Europe, US, and India) Extensive familiarity with Cloud platforms, with Google Cloud preferred. Expert understanding of various database technologies (OLTP vs OLAP vs graphs vs blob storage vs ..) and extensive experience working with several of them. What We Offer Opportunities to lead and shape high-impact AI initiatives within a rapidly growing global data organization. A learning-focused environment that champions technical leadership, mentorship, and breakthrough innovation within vibrant technical communities and innovation labs. A collaborative culture that empowers senior engineers to drive ownership, foster clarity, and continuously elevate standards A unique opportunity to lead the development and deployment of cutting-edge AI solutions with significant impact. A collaborative, intellectually stimulating, and innovative work environment in Waregem, Flanders, Belgium. Opportunities for continuous learning, professional growth, and leadership development in the rapidly evolving field of AI. A competitive salary and comprehensive benefits package. The chance to shape the future of AI within the organization and mentor the next generation of AI engineers. Whats In It For You A family atmosphere , people-centric culture, where your emotional and physical well-being matters. A company of great colleagues with a global mindset, where you feel welcomed from day one. A competitive salary , medical insurance for family , retirement benefits Healthy work life balance Internal career opportunities, professional development, including access to LinkedIn Learning and many in-house/external training courses Job security working for a global company with strong presence & commitment in India. PEOPLE ARE AT OUR HEART TVH is a global business with a family atmosphere, where people are at the center. We value clarity, mutual respect, kindness and open communication. Our people are down-to-earth, easy to work and engage with. We welcome differences and celebrate new ideas. About Tvh TVH is a parts specialist for quality parts and accessories for material handling, industrial vehicles, and construction and agricultural equipment. Working at TVH is opting for a company that excels as an international market leader and is well-known for its unstoppable craving for innovation. Show more Show less

Description and Requirements Key Responsibilities : Lead end-to-end transitions of AI PoCs into production environments, managing the entire process from testing to final deployment. Configure, install, and validate AI systems using key platforms, including VMware ESXi and vSphere for server virtualization, Linux (Ubuntu/RHEL) and Windows Server for operating system integration, Docker and Kubernetes for containerization and orchestration of AI workloads. Conduct comprehensive performance benchmarking and AI inferencing tests to validate system performance in production. Optimize deployed AI models for accuracy, performance, and scalability to ensure they meet production-level requirements and customer expectations. Serve as the primary technical lead/SME for the AI POC deployment in enterprise environments, focusing on AI solutions powered by Nvidia GPUs. Work hands-on with Nvidia AI Enterprise and GPU-accelerated workloads, ensuring efficient deployment and model performance using frameworks such as PyTorch and TensorFlow. Lead technical optimizations aimed at resource efficiency, ensuring that models are deployed effectively within the customer's infrastructure. Ensure the readiness of customer environments to handle, maintain, and scale AI solutions post-deployment. take ownership of AI project deployments, overseeing all phases from planning to final deployment, ensuring that timelines and deliverables are met. Collaborate with stakeholders, including cross-functional teams (e.g., Lenovo AI Application, solution architects), customers, and internal resources to coordinate deployments and deliver results on schedule. Implement risk management strategies and develop contingency plans to mitigate potential issues such as hardware failures, network bottlenecks, and software incompatibilities. Maintain ongoing, transparent communication with all relevant stakeholders, providing updates on project status and addressing any issues or changes in scope. Experience : Overall experience 7-10 years Relevant experience of 2-4 years in deploying AI/ML models/ AI solutions using Nvidia GPUs in enterprise production environments. Demonstrated success in leading and managing complex AI infrastructure projects, including PoC transitions to production at scale. Technical Expertise: Experience in the area of Retrieval Augmented Generation (RAG), NVIDIA AI Enterprise, NVIDIA Inference Microservices (NIMs), Model Management, Kubernetes Extensive experience with Nvidia AI Enterprise, GPU-accelerated workloads, and AI/ML frameworks such as PyTorch and TensorFlow. Proficient in deploying AI solutions across enterprise platforms, including VMware ESXi, Docker, Kubernetes, and Linux (Ubuntu/RHEL) and Windows Server environments. MLOps proficiency with hands-on experience using tools such as Kubeflow, MLflow, or AWS SageMaker for managing the AI model lifecycle in production. Strong understanding of virtualization and containerization technologies to ensure robust and scalable deployments.

Develop and implement machine learning and deep learning models. Integrate AI solutions into applications and systems. Optimize model performance, scalability, and accuracy. Research and apply the latest AI tools, frameworks,

As a Senior Applied ML Engineer at Cozeva, you will play a crucial role in bridging the gap between research and production, ensuring that Cozeva's AI innovations are successfully deployed, monitored, and scaled within the SaaS platform. Your expertise in machine learning models and enterprise-scale software systems will be instrumental in turning prototypes into high-performing, production-grade AI features that drive value-based care. Key Responsibilities: - Model Integration & Deployment: - Embed various ML/AI models (LLMs, NLP, risk models, forecasting) into Cozeva's software workflows and APIs. - Build and maintain production pipelines for training, evaluation, and deployment on cloud-native infrastructure. - Scalable Systems Engineering: - Design and manage distributed data pipelines for claims, clinical, and EHR data. - Ensure the performance, reliability, and cost efficiency of AI workloads on Aurora MySQL, Redshift, S3, EC2/K8s. - MLOps Practices: - Implement CI/CD for ML, including model versioning, automated retraining, monitoring, and rollback. - Monitor live model performance, drift, and fairness to comply with Cozeva's AI Governance Framework v1.1. - Applied Problem Solving: - Collaborate with AI Scientists to deploy models for NLP abstraction of clinical data, risk stratification, hospitalization/ED prediction, and member engagement. - Collaboration: - Work closely with data engineers, SDEs, and product teams to deliver AI-driven features in Cozeva's SaaS stack. Qualifications Required: - Bachelors or Masters in Computer Science, AI/ML, or related field. - 5-8 years of experience in building and deploying ML models into production. - Strong software engineering skills in Python, SQL, Docker/Kubernetes, and distributed systems. - Experience with ML frameworks such as PyTorch, TensorFlow, Hugging Face, and MLOps tools like MLflow, Kubeflow, SageMaker, or equivalent. - Proficiency in cloud infrastructure, preferably AWS (S3, EC2, RDS/Aurora, Redshift, IAM). - Proven track record of delivering production-ready ML/AI features at scale. Why Join Cozeva By joining Cozeva, you will have the opportunity to: - Make a meaningful impact on health equity and outcomes for millions of patients. - Deploy cutting-edge models directly into a SaaS platform. - Collaborate closely with the CTO, scientists, and engineering leaders. - Work with top-tier AI talent globally in a culture of transparency, collaboration, and impact.,

You will be responsible for the following key roles: - AI/ML Developer (2-3 years experience): - Develop, fine-tune, and deploy AI/ML models. - Senior AI/ML Developer (5-7 years experience): - Lead AI/ML projects, optimize large-scale models, and mentor junior developers. Qualifications required for the role include: - B.Tech / M.Tech in Computer Science, AI, or related fields. - AI/ML Developer: - 2-3 years of experience in AI/ML development, strong in Python, TensorFlow/PyTorch, data engineering. - Senior AI/ML Developer: - 5-7 years of experience with expertise in LMs, deep learning, scalable architectures, and performance optimization. - Experience with MLOps tools MLflow, Kubeflow, Docker, Kubernetes) and cloud-based AI solutions. - Strong understanding of data structures, algorithms, and model deployment. Join a team that is actively shaping the future of AI-powered applications!,

As an AI/ML Developer, you will be responsible for utilizing programming languages such as Python for AI/ML development. Your proficiency in libraries like NumPy, Pandas for data manipulation, Matplotlib, Seaborn, Plotly for data visualization, and Scikit-learn for classical ML algorithms will be crucial. Familiarity with R, Java, or C++ is a plus, especially for performance-critical applications. Your role will involve building models using Machine Learning & Deep Learning Frameworks such as TensorFlow and Keras for deep learning, PyTorch for research-grade and production-ready models, and XGBoost, LightGBM, or CatBoost for gradient boosting. Understanding model training, validation, hyperparameter tuning, and evaluation metrics like ROC-AUC, F1-score, precision/recall will be essential. In the field of Natural Language Processing (NLP), you will work with text preprocessing techniques like tokenization, stemming, lemmatization, vectorization techniques such as TF-IDF, Word2Vec, GloVe, and Transformer-based models like BERT, GPT, T5 using Hugging Face Transformers. Experience with text classification, named entity recognition (NER), question answering, or chatbot development will be required. For Computer Vision (CV), your experience with image classification, object detection, segmentation, and libraries like OpenCV, Pillow, and Albumentations will be utilized. Proficiency in pretrained models (e.g., ResNet, YOLO, EfficientNet) and transfer learning is expected. You will also handle Data Engineering & Pipelines by building and managing data ingestion and preprocessing pipelines using tools like Apache Airflow, Luigi, Pandas, Dask. Experience with structured (CSV, SQL) and unstructured (text, images, audio) data will be beneficial. Furthermore, your role will involve Model Deployment & MLOps where you will deploy models as REST APIs using Flask, FastAPI, or Django, batch jobs, or real-time inference services. Familiarity with Docker for containerization, Kubernetes for orchestration, and MLflow, Kubeflow, or SageMaker for model tracking and lifecycle management will be necessary. In addition, your hands-on experience with at least one cloud provider such as AWS (S3, EC2, SageMaker, Lambda), Google Cloud (Vertex AI, BigQuery, Cloud Functions), or Azure (Machine Learning Studio, Blob Storage) will be required. Understanding cloud storage, compute services, and cost optimization is essential. Your proficiency in SQL for querying relational databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), and familiarity with big data tools like Apache Spark, Hadoop, or Databricks will be valuable. Experience with Git and platforms like GitHub, GitLab, or Bitbucket will be essential for Version Control & Collaboration. Familiarity with Agile/Scrum methodologies and tools like JIRA, Trello, or Asana will also be beneficial. Moreover, you will be responsible for writing unit tests and integration tests for ML code and using tools like pytest, unittest, and debuggers to ensure the quality of the code. This position is Full-time and Permanent with benefits including Provident Fund and Work from home option. The work location is in person.,

As a Senior Backend Engineer at our company, you will be responsible for designing, developing, and maintaining the infrastructure powering our generative AI applications. You will collaborate with AI engineers, platform teams, and product stakeholders to build scalable and reliable backend systems supporting AI model deployment, inference, and integration. This role will challenge you to combine traditional backend engineering expertise with cutting-edge AI infrastructure challenges to deliver robust solutions at enterprise scale. - Design and implement scalable backend services and APIs for generative AI applications using microservices architecture and cloud-native patterns. - Build and maintain model serving infrastructure with load balancing, auto-scaling, caching, and failover capabilities for high-availability AI services. - Deploy and orchestrate containerized AI workloads using Docker, Kubernetes, ECS, and OpenShift across development, staging, and production environments. - Develop serverless AI functions using AWS Lambda, ECS Fargate, and other cloud services for scalable, cost-effective inference. - Implement robust CI/CD pipelines for automated deployment of AI services, including model versioning and gradual rollout strategies. - Create comprehensive monitoring, logging, and alerting systems for AI service performance, reliability, and cost optimization. - Integrate with various LLM APIs (OpenAI, Anthropic, Google) and open-source models, implementing efficient batching and optimization techniques. - Build data pipelines for training data preparation, model fine-tuning workflows, and real-time streaming capabilities. - Ensure adherence to security best practices, including authentication, authorization, API rate limiting, and data encryption. - Collaborate with AI researchers and product teams to translate AI capabilities into production-ready backend services. - Strong experience with backend development using Python, with familiarity in Go, Node.js, or Java for building scalable web services and APIs. - Hands-on experience with containerization using Docker and orchestration platforms including Kubernetes, OpenShift, and AWS ECS in production environments. - Proficient with cloud infrastructure, particularly AWS services (Lambda, ECS, EKS, S3, RDS, ElastiCache) and serverless architectures. - Experience with CI/CD pipelines using Jenkins, GitLab CI, GitHub Actions, or similar tools, including Infrastructure as Code with Terraform or CloudFormation. - Strong knowledge of databases including PostgreSQL, MongoDB, Redis, and experience with vector databases for AI applications. - Familiarity with message queues (RabbitMQ, Apache Kafka, AWS SQS/SNS) and event-driven architectures. - Experience with monitoring and observability tools such as Prometheus, Grafana, DataDog, or equivalent platforms. - Knowledge of AI/ML model serving frameworks like MLflow, Kubeflow, TensorFlow Serving, or Triton Inference Server. - Understanding of API design principles, load balancing, caching strategies, and performance optimization techniques. - Experience with microservices architecture, distributed systems, and handling high-traffic, low-latency applications. - Bachelors degree in computer science, Engineering, or related technical field, or equivalent practical experience. - 4+ years of experience in backend engineering with focus on scalable, production systems. - 2+ years of hands-on experience with containerization, Kubernetes, and cloud infrastructure in production environments. - Demonstrated experience with AI/ML model deployment and serving in production systems.,

As a Staff Software Developer specializing in Machine Learning for EDA Systems at Qualcomm India Private Limited, you will play a crucial role in designing and implementing cutting-edge machine learning techniques in the realm of Electronic Design Automation (EDA). Your primary responsibilities will revolve around leading software development initiatives, guiding a team of engineers and ML practitioners, and ensuring the seamless integration of ML into semiconductor design workflows. Below are the key details of the job: **Role Overview:** You will be responsible for designing and developing end-to-end ML-integrated EDA software platforms, leading a team of engineers, refining software development processes, spearheading advanced algorithm design, and overseeing deployment strategies. **Key Responsibilities:** - Design and develop ML-integrated EDA software platforms for scale, performance, and adaptability. - Guide and mentor a team of software engineers and ML practitioners. - Own and refine software development processes and embed best practices. - Spearhead advanced algorithm design for automation and optimization in EDA flows. - Engineer reliable and scalable data pipelines for ML model development. - Oversee full-stack deployment strategies for ML applications. - Establish and maintain comprehensive ML Ops infrastructure. - Experience in system integration and testing initiatives. - Manage CI/CD pipelines and ensure system scalability. **Required Skills & Experience:** - 8+ years of software development experience with a focus on ML and EDA tools. - Proficiency in Python, C++, and frameworks like TensorFlow, PyTorch, or Scikit-learn. - Familiarity with CI/CD tools and MLOps platforms. - Deep knowledge of EDA tools and flows. - Skilled in data engineering and cloud/on-prem infrastructures. - Strong communication and leadership skills. **Preferred Qualifications:** - MS or PhD in Computer Science, Electrical Engineering, or related field. - Expertise in advanced ML techniques applied to EDA. - Contributions to open-source, patents, or technical publications related to ML-driven design automation. Please note that Qualcomm is an equal opportunity employer and is committed to providing accessible processes for individuals with disabilities. For further information about this role, please contact Qualcomm Careers.,

My profile - linkedin.com/in/yashsharma1608 Position : AI Architect ( Gen AI ) Experience : 8 - 10 years Notice Period : Immediate to 15 days. Budget upto - 45 to 50 LPA Location : Bangalore. Note : - (any developer with minimum 3 to 4 years into AI), SaaS company mandatory. Product Based company Mandatory Discuss the feasibility of AI/ML use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings Design and implement robust ML infrastructure and deployment pipelines Establish comprehensive MLOps practices for model training, versioning, and deployment Lead the development of HR-specialized language models (SLMs) Implement model monitoring, observability, and performance optimization frameworks Develop and execute fine-tuning strategies for large language models Create and maintain data quality assessment and validation processes Design model versioning systems and A/B testing frameworks Define technical standards and best practices for AI development Optimize infrastructure for cost, performance, and scalability Required Qualifications 7+ years of experience in ML/AI engineering or related technical roles 3+ years of hands-on experience with MLOps and production ML systems Demonstrated expertise in fine-tuning and adapting foundation models Strong knowledge of model serving infrastructure and orchestration Proficiency with MLOps tools (MLflow, Kubeflow, Weights & Biases, etc.) Experience implementing model versioning and A/B testing frameworks Strong background in data quality methodologies for ML training Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face) Experience with cloud-based ML platforms (AWS, Azure, Google Cloud) Proven track record of deploying ML models at scale Preferred Qualifications Experience developing AI applications for enterprise software domains Knowledge of distributed training techniques and infrastructure Experience with retrieval-augmented generation (RAG) systems Familiarity with vector databases (Pinecone, Weaviate, Milvus) Understanding of responsible AI practices and bias mitigation Bachelor's or Master's degree in Computer Science, Machine Learning, or related field What We Offer Opportunity to shape AI strategy for a fast-growing HR technology leader Collaborative environment focused on innovation and impact Competitive compensation package Professional development opportunities Flexible work arrangements Qualified candidates who are passionate about applying cutting-edge AI to transform HR technology are encouraged to apply

Qualifications: Bachelors degree in Computer Science or a related field Significant experience in a DevOps or Site Reliability Engineering(SRE) role Strong expertise in Kubernetes, including deployment, scaling, andmanagement of containerized applications Proven experience in setting up and managing open-source platforms like Elasticsearch,Kafka, and Grafana Solid understanding of infrastructure as code principles and experience with tools likeTerraform or Ansible Experience with CI/CD tools (eg, Jenkins, GitLab CI/CD, CircleCI) Strong scripting skills (eg, Python, Bash) Excellent troubleshooting and problem-solving abilities Strong communication and collaboration skills, with the ability to work effectively withML and development teams A strong desire to learn and grow in the field of MLOps Bonus Points: Experience with cloud platforms (eg, AWS, GCP, Azure) Familiarity with MLOps tools and concepts (eg, MLflow, Kubeflow) Experience with data engineering tools Qualifications: Bachelors degree in Computer Science or a related field Significant experience in a DevOps or Site Reliability Engineering(SRE) role strong expertise in Kubernetes, including deployment, scaling, andmanagement of containerized applications Proven experience in setting up and managing open-source platforms like Elasticsearch,Kafka, and Grafana Solid understanding of infrastructure as code principles and experience with tools like Terraform or Ansible

You will be responsible for designing and delivering a portal web application as a Sr. Tech Lead in an agile development lifecycle. With a minimum of 8 years of experience in web application development, you should demonstrate expertise in Java, Springboot, Microservices, and Javascript Frameworks such as Angular and REACT. Cloud-based development experience is also necessary for this role. Your key responsibilities will include designing and architecting Java-based applications, leading and mentoring development teams, collaborating with stakeholders to gather requirements, developing high-quality code in Java, identifying technical debt, and staying updated with emerging technologies. You will also be involved in developing web UI pages using JavaScript frameworks, integrating with service REST APIs, and enhancing back-end components and services for improved performance. To succeed in this role, you should possess strong communication, collaboration, and problem-solving skills, along with a track record of delivering production-grade systems. Experience with agile scrum methodologies, Javascript, HTML5, CSS3, and Java development based on Microservices Patterns is essential. Proficiency in technologies like Java, Spring Framework, Spring Boot, SQL, API Integration (REST, SOAP), JSON, XML, Docker, Kubernetes, and database systems like SQL and non-SQL is required. Additionally, familiarity with enterprise solutions, Cross Cutting Concern implementation, Microservices architecture, Cloud Technologies, AI ML techniques, and application integration is beneficial. Experience with agile tools such as Atlassian JIRA, Rally, TFS, Version One, code repositories like subversion/git, and Docker is highly preferred. Exposure to web server technologies like nginx, Tomcat, and hands-on experience in leading technical teams will be advantageous. The ideal candidate will have a Bachelor's degree, a minimum of 7 years of relevant experience, and a proactive attitude towards learning and building technical solutions. Strong attention to detail, willingness for research and development, and the ability to multitask and prioritize in a complex environment are crucial for excelling in this role.,

As an AI/ML professional at Voxai, you will play a crucial role in architecting and implementing Large Language Model (LLM)-driven solutions that revolutionize customer experience in the contact center domain. Your responsibilities will include designing and optimizing Retrieval-Augmented Generation (RAG) pipelines, recommending AI/ML architectures, developing AI agents, experimenting with Agentic AI systems, and building MLOps pipelines. You will collaborate with cross-functional teams to deliver real-world AI applications, analyze contact center data to derive actionable insights, and mentor junior engineers. Your qualifications should include a strong foundation in Computer Science, proficiency in Statistics and Machine Learning, hands-on experience in deploying ML models, familiarity with LLMs and vector databases, and expertise in MLOps tools and cloud-native deployment. Additionally, excellent problem-solving, analytical, and communication skills are essential for this role. Ideally, you should hold a Masters or Ph.D. in Computer Science or a related field, have experience in enterprise software or customer experience platforms, and be able to work effectively in a fast-paced, collaborative environment. Join Voxai to be at the forefront of CX innovation and make a significant impact in the tech industry.,

This role provides you with the opportunity to utilize your cloud-native deployment expertise and experience in modern AI platforms to develop scalable ML pipelines, LLM-based applications, and intelligent agent frameworks. You will be instrumental in hastening the delivery of innovative solutions for telecom, enterprise, and next-generation autonomous networks. If you enjoy a challenging and fulfilling work environment, we invite you to apply. Your Responsibilities and Learning Opportunities: - Implement MLOps best practices for CI/CD, model deployment, and monitoring. - Focus on fine-tuning, prompt engineering, RAG pipelines, and LLM monitoring. - Enhance AI/ML systems with Agentic AI frameworks for autonomous decision-making and workflow automation. - Manage structured, semi-structured, and unstructured data through ingestion, preprocessing, and feature engineering. - Collaborate with cross-functional teams to convert use cases into production-ready deployments. - Utilize platforms such as GCP (Vertex AI), RedHat OpenShift AI, and Kubeflow in multi-cloud/hybrid environments. Key Skills and Experience: You should possess: - Bachelor's/Master's degree in computer science, Data Engineering, AI/ML, or a related field. - Over 10 years of experience in AI/ML engineering, with a focus of at least 5 years on MLOps. - Proficiency in Python, PyTorch, TensorFlow, Scikit-learn, and SQL. - Experience with MLOps pipelines like Kubeflow, MLflow, and Vertex AI. Desired Skills: It would be beneficial if you also had: - Experience with Ab-initio data management platforms. - Familiarity with telecom data products and autonomous networks. - Knowledge of vector databases and retrieval-augmented generation (RAG). - Contributions to open-source AI/ML or GenAI frameworks. About Us: Nokia is dedicated to fostering innovation and technology leadership in mobile, fixed, and cloud networks. A career with us will positively impact people's lives and contribute to building the capabilities necessary for a more productive, sustainable, and inclusive world. We strive to cultivate an inclusive work environment where new ideas are welcomed, risks are encouraged, and authenticity is valued. What We Offer: At Nokia, you will have access to continuous learning opportunities, well-being programs to support your mental and physical health, participation in employee resource groups, mentorship programs, and collaboration with highly diverse teams in an inclusive culture where individuals thrive and are empowered. Nokia is committed to inclusion and is an equal opportunity employer. Recognition: Nokia has been recognized for its commitment to inclusion and equality by: - Being named one of the World's Most Ethical Companies by Ethisphere. - Inclusion in the Gender-Equality Index by Bloomberg. - Workplace Pride Global Benchmark. Join us and become a part of a company where you will feel included and empowered to achieve success. About The Team: As the growth engine of Nokia, we add value for communication service providers and enterprise customers by leading the transition to cloud-native software and as-a-service delivery models. Our team, consisting of dreamers, doers, and disruptors, continuously push boundaries from impossible to possible.,

Are you seeking a rewarding career opportunity at one of India's Top 25 Best Workplaces in the IT industry Would you like to contribute to the success of one of the fastest-growing IT services companies and excel in your professional endeavors Are you looking to thrive in an award-winning work environment that recognizes and nurtures your talents and career aspirations Look no further than Iris Software. Iris Software is dedicated to becoming the most trusted technology partner for its clients and the preferred choice for top industry professionals to realize their full potential. With a workforce of over 4,300 associates spread across India, the U.S.A, and Canada, we specialize in enabling technology-driven transformation for enterprise clients in sectors such as financial services, healthcare, transportation & logistics, and professional services. Our projects involve working on intricate, mission-critical applications utilizing cutting-edge technologies like high-value complex Application & Product Engineering, Data & Analytics, Cloud services, DevOps, Data & MLOps, Quality Engineering, and Business Automation. Joining Iris Software means being part of a culture that appreciates, inspires, and encourages you to be your best self. We foster an environment where employees are valued, have opportunities to explore their potential, and are supported in their professional and personal growth. Job Description: - Must have 6-8 years of overall experience with a minimum of 3 years in ML Ops engineering. - Proficiency in Python is essential, along with hands-on experience in machine learning libraries such as TensorFlow, PyTorch, and scikit-learn. - Extensive familiarity with ML Ops frameworks like DataIku, Kubeflow, MLflow, and TensorFlow Extended (TFX). - Strong background in deploying and managing machine learning models within AWS environments. - Skilled in managing CI/CD pipelines for ML workflows using tools like Jenkins, GitLab, and CircleCI. - Hands-on experience with containerization (Docker) and orchestration (Kubernetes) technologies for model deployment. - Mandatory skills include SageMaker, DataIku, Python, PySpark, and various AWS services; Good to have: AWS CDK. Mandatory Competencies: - Data Science - Machine Learning (ML) - Python - Communication - DevOps/Configuration Mgmt - Jenkins - Github - Kubernetes - Big Data - Pyspark - Cloud - AWS - AWS Lambda, AWS EventBridge, AWS Fargate - Data Science and Machine Learning - AI/ML - Data Science and Machine Learning - Python - DevOps/Configuration Mgmt - GitLab, Github, Bitbucket - DevOps/Configuration Mgmt - Containerization (Docker, Kubernetes) Perks and Benefits for Irisians: At Iris Software, we provide a range of world-class benefits that cater to the financial, health, and well-being needs of our associates. From comprehensive health insurance and competitive salaries to flexible work arrangements and continuous learning opportunities, we are committed to creating a supportive and rewarding work environment. Join us at Iris Software and discover the difference of working for a company that prioritizes the success and happiness of its employees.,

As a Python Developer in this position, you will be responsible for utilizing your expertise in Python programming to design and develop solutions for various projects. You should have a strong educational background in Computer Engineering with at least 10+ years of experience in the field. The role offers the flexibility of working from home on a full-time basis with a work schedule aligned with US timings. Your key responsibilities will include working on Load Balancers, Distributed Systems, Sticky Sessions, SQL and NoSQL Databases, Caching mechanisms like Redis and Memcached, and handling a large number of requests per second efficiently. Additionally, you should have experience in DevOps, Data Engineering, or ML Ops roles, along with proficiency in Terraform, cloud providers like AWS, GCP, or Azure, and containerization tools like Docker. You will be expected to have hands-on knowledge of ML experiment platforms such as MLflow, Kubeflow, Weights & Biases, as well as workflow execution frameworks like Kubeflow and Apache Airflow. A solid understanding of CI/CD principles, Git workflows, and infrastructure testing is essential for this role. Moreover, your communication skills should be excellent to collaborate effectively with Data Scientists, Software Engineers, and Security teams. In addition to a competitive salary, this position offers benefits such as cell phone reimbursement, leave encashment, paid sick time, paid time off, and Provident Fund. This is a full-time, permanent job opportunity that provides the convenience of working remotely while contributing to impactful projects.,

Job Description * Role & Responsibilities: Deploy and manage Kubernetes clusters with autoscaling using Karpenter . Implement event-driven scaling with KEDA for dynamic workloads. Optimize GPU workloads for compute-intensive tasks in Kubernetes. Build and maintain Grafana dashboards with Prometheus integration. Develop Python scripts for automation, IaC, and CI/CD pipelines. Manage containerized deployments with Docker and Helm . Collaborate on CI/CD workflows using Jenkins and GitLab. Troubleshoot production issues ensuring high availability . Mentor junior engineers and enforce DevOps best practices. Must-Have Skills: Golang (Mandatory) and Python expertise. Strong hands-on with KEDA , Kubernetes, Docker, Helm. CI/CD exposure with Jenkins / GitLab. Knowledge of Grafana, Prometheus for monitoring. Good-to-Have Skills: AI/ML architecture knowledge (neural networks, inference). Experience with ML deployment tools ( Kubeflow, TensorFlow Serving ). Familiarity with ML model development ( PyTorch, scikit-learn ).

At EY, you'll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture, and technology to become the best version of you. And we're counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. We are looking for Manager Level Architects with expertise in Generative AI who can work on end-to-end pipelines, enabling data curation, building and fine-tuning Generative AI models, and deploying them into scalable production streams. This is a fantastic opportunity to be part of a leading firm whilst being instrumental in the growth of a new service offering. This role demands a highly technical, extremely hands-on experience who will work closely with our EY Partners and external clients to develop new business as well as drive other initiatives on different business needs across different Customer and SC&O domains. The ideal candidate must have a good understanding of the problems and use cases that can be solved with Gen AI models with closest accuracy with Supply Chain industry knowledge and proven experience in delivering solutions to different lines of business and technical leadership. Your key responsibilities include collaborating with EY Supply Chain & Operations stakeholders and data engineering teams to enable high-quality data curation for model training and evaluation, designing, developing, and fine-tuning Generative AI models, implementing solutions for data pre-processing, tokenization, and dataset augmentation, deploying Generative AI models on cloud platforms or edge devices, working on MLOps pipelines, conducting performance benchmarking, hyperparameter tuning, and optimization, and staying updated on the latest trends and advancements in Generative AI. To qualify for the role, you must have 6-10 years of experience in ML, MLOps, Generative AI LLMs as Developer/Lead/Architect, expertise in Data Engineering, Data transformation, curation, feature selection, ETL Mappings, Data Warehouse concepts, thorough knowledge in SQL, Python, PySpark, Spark, and other languages, experience in developing end-to-end GenAI solutions with the capability to migrate the solution for Production, knowledge of Cloud like Azure, AWS, GCP, etc., knowledge of frameworks like LangChain, Hugging Face, Azure ML Studio, Azure, knowledge of data modeling and Vector DB management, and experience in designing and developing complex flows and processes. Skills and attributes for success include proficiency in Python with focus on AI/ML libraries and frameworks like LangChain, TensorFlow, PyTorch, or Hugging Face Transformers, experience with data pipelines and tools like Spark, Snowflake, or Databricks, strong hands-on experience deploying AI models on cloud platforms, expertise in Model Development with in-depth knowledge of LLMs, and the ability to mentor developers and contribute to architectural decisions. Ideally, you'll also have a strong understanding of Customer and Supply Chain process and operations, knowledge of Programming concepts, Cloud Concepts, LLM Models, design, and coding, expertise in data handling to resolve any data issues as per client needs, experience in designing and developing DB objects and Vector DBs, experience of creating complex SQL queries, PySpark code, Python Scripting for retrieving, manipulating, checking, and migrating complex datasets, and good verbal and written communication in English. At EY, you'll be part of a market-leading, multi-disciplinary team of professionals, and you'll have opportunities to work with leading businesses across a range of industries. EY offers support, coaching, and feedback, opportunities to develop new skills and progress your career, and the freedom and flexibility to handle your role in a way that's right for you.,