Kubeflow Jobs in Bengaluru

Job Title: ML Ops Engineer Location: Bangalore Department: Data Science Employee Type: Full-time Experience Required: 3 5 years Position Overview We are seeking an experienced and motivated ML Ops Engineer to join our Data Science team. In this role, you will be responsible for deploying, monitoring, and maintaining machine learning models in production environments. You will work closely with data scientists, engineers, and product teams to ensure models are scalable, reliable, and aligned with business objectives. This role is ideal for professionals who are passionate about building robust ML pipelines and bringing machine learning solutions into real-world applications at scale. Key Responsibilities Deploy and manage machine learning models in production environments, ensuring scalability, reliability, and performance. Build and maintain MLOps pipelines using platforms like Databricks and MLflow. Monitor model performance, accuracy, and health; implement alerting and diagnostics as needed. Develop and maintain RESTful APIs using Python frameworks such as Flask or Django to serve ML models. Optimize data workflows and collaborate with engineering teams to improve model integration and performance. Design strategies for automated model retraining, deployment, and version control. Write clean, maintainable, and efficient code using Python, adhering to OOP principles and best practices. Write complex queries using SQL and work with NoSQL databases to support data pipelines and feature stores. Leverage Python libraries such as PySpark, Pandas, scikit-learn, SQLAlchemy, and Requests. Minimum Qualifications Bachelor s or Master s degree in Computer Science, Statistics, Econometrics, Operations Research, or a related technical field. 3 5 years of experience in building, deploying, and monitoring machine learning solutions in production. Must-Have Skills Experience with Databricks and MLflow for model training and deployment. Proven expertise in machine learning model deployment and monitoring in live environments. Strong programming skills in Python, with solid understanding of data structures, algorithms, and OOP concepts. Experience developing RESTful APIs using Flask or Django. Proficient in SQL and NoSQL database operations. Hands-on knowledge of libraries such as Pandas, PySpark, scikit-learn, SQLAlchemy, and Requests. Strong analytical, problem-solving, and debugging skills. Good-to-Have Skills Experience with Kafka streaming and batch processing. Familiarity with CI/CD pipelines and version control systems like Git. Understanding of Python multiprocessing, worker/queue systems, and asynchronous/event-driven programming. This is a unique opportunity to work at the intersection of machine learning and DevOps. You'll play a critical role in operationalizing AI models and making them a core part of our product offerings. If you enjoy building scalable systems and solving real-world ML engineering challenges, we d love to meet you. Qualification : Bachelors or Masters degree in Computer Science, Statistics, Econometrics, Operations Research, or a related technical field

Distinguished Engineer Machine Learning Engineering Location: Bangalore Company: Capital One India About Us At Capital One India, we re redefining how technology powers financial services. Our teams work in a fast-paced, intellectually rigorous environment to tackle complex business challenges at scale. By harnessing the power of advanced analytics, data science, and machine learning, we create innovative, patentable solutions that transform customer experiences and drive the business forward. Team Overview: Machine Learning Experience (MLX) The MLX team leads Capital One s mission to build scalable, well-managed ML systems and platforms. We empower teams across the enterprise to develop, govern, and deploy machine learning models efficiently, securely, and at scale. From automated model governance to observability platforms, MLX enables end-to-end ML lifecycle management laying the foundation for AI-driven innovation across the organization. Role Overview We re looking for a Distinguished Engineer Machine Learning Engineering to join our MLX team. In this high-impact role, you'll architect and implement the platforms and tools that support model observability, automated governance, and ML model deployment at scale. This is an opportunity to drive enterprise-wide innovation and shape how ML is integrated into Capital One s core business systems. What You ll Do Design and build systems that capture and analyze large-scale model and feature metadata, including training metrics and runtime performance, to power model observability and governance automation. Partner with cross-functional teams including product managers, designers, and platform engineers to create scalable solutions that accelerate ML model lifecycle management. Lead efforts to enable automated governance decisions for ML models, ensuring compliance, auditability, and operational integrity. Architect and implement high-performance data pipelines that feed ML models with real-time and batch data. Contribute to the design and implementation of cloud-native ML systems using tools such as AWS, Kubernetes, and Terraform. Write clean, scalable, production-grade code in languages like Python, Go, or Java. Implement CI/CD pipelines, testing frameworks, and monitoring systems for ML applications. Drive the adoption of best practices in ML Ops, observability, and platform resilience. Basic Qualifications Master s Degree in Computer Science or related field. 15+ years of experience in software engineering or solution architecture. 10+ years building data-intensive, distributed computing systems. 10+ years programming in Python, Go, or Java. 8+ years of hands-on experience with industry-leading ML frameworks (e.g., Scikit-learn, TensorFlow, PyTorch, Dask, Spark). Preferred Qualifications PhD or Master's in Computer Science, Electrical Engineering, Mathematics, or related field. 5+ years of experience building, scaling, and optimizing production ML systems. Deep expertise in data preparation, feature engineering, and ML pipeline optimization. 10+ years writing performant, maintainable, and resilient production code. Strong experience deploying ML solutions on public cloud platforms (AWS, Azure, GCP). Expertise in distributed systems, file systems, or multi-node databases. Open-source contributor to ML tools or libraries. Published work in ML (papers, patents, blogs, etc.). 5+ years of experience in ML Ops (using MLflow, TFX, Kubeflow, etc.). Experience with LLMs and Generative AI applications (open-source or commercial models). Proven experience designing production-ready observability platforms for ML applications. Be at the forefront of building scalable, secure, and enterprise-grade ML platforms. Shape the future of AI and ML adoption in a top-tier financial institution. Collaborate with world-class engineers and data scientists. Solve real-world problems with high business impact. Thrive in a diverse, inclusive, and innovation-focused culture. Qualification : PhD or Master's in Computer Science, Electrical Engineering, Mathematics, or related field

Principal Associate Full Stack Engineering (GenAI Observability) Location: Bangalore Company: Capital One India About Us At Capital One India, we re tackling some of the most complex problems in financial services using machine learning, advanced analytics, and cloud-first engineering. Our mission is to build cutting-edge, patentable solutions that transform customer experiences, enhance operational efficiency, and ensure robust risk and compliance standards. We re a team of makers, breakers, doers, and disruptors obsessed with turning data into real-world impact at scale. About the Team Machine Learning Experiences (MLX) The MLX team is pioneering the future of model governance, ML observability, and Generative AI infrastructure at Capital One. We re enabling teams to seamlessly deploy ML and GenAI models at scale, with full visibility into performance, health, compliance, and ethical usage. This is the platform powering the next generation of AI-driven financial products across the company. About the Role We re looking for a Principal Associate Full Stack Engineer to lead the development of observability platforms for Generative AI systems. You ll be part of a cross-functional team focused on governance automation, LLM monitoring, and intelligent diagnostics using telemetry data, metadata, and advanced analytics. You ll design systems to collect, analyze, and visualize performance data from our large-scale GenAI infrastructure, helping data scientists and engineers make faster, safer decisions. What You ll Do Lead architecture and development of observability tools and dashboards for monitoring GenAI models and platform health. Design and build core APIs and SDKs to instrument large language models (LLMs) and foundational models (training, fine-tuning, prompting stages). Integrate Generative AI to enable observability features like anomaly detection, predictive analytics, and copilot-assisted troubleshooting. Partner with platform, MLOps, and governance teams to ingest and analyze telemetry, metadata, and runtime metrics at scale. Drive development of tools to ensure compliance with AI ethics, data governance, and industry regulations. Collaborate with product, design, and research to turn complex requirements into scalable, cloud-native software solutions. Lead proof-of-concept initiatives to test and showcase how GenAI can improve platform observability and decision-making. Contribute to the open-source community and stay at the forefront of GenAI and ML infrastructure evolution. Basic Qualifications Bachelor s or Master s degree in Computer Science, Engineering, or related field 4+ years of experience building distributed, data-intensive systems using microservices architecture 4+ years of experience in backend development with Python, Go, or Java 4+ years of expertise with observability stacks (Prometheus, Grafana, ELK) and adapting them for AI systems Strong knowledge of OpenTelemetry, and experience building custom SDKs and APIs 5+ years of hands-on experience with Generative AI models, especially applied to observability, governance, or compliance 2+ years of experience with cloud platforms such as AWS, Azure, or GCP Preferred Qualifications 4+ years building and optimizing ML systems in production environments 3+ years of experience with MLOps tools like MLflow, Kubeflow, or commercial platforms Experience with GenAI frameworks and libraries like LangChain, Haystack, and vector databases (FAISS, Chroma, OpenSearch) Familiarity with emerging observability tools for LLMs such as Langfuse, Phoenix, Helicone, or OpenInference Contributor to open-source GenAI or ML infrastructure projects Author or co-author of published work in AI/ML observability, governance, or performance monitoring Experience with PyTorch, TensorFlow, Spark, or Dask Knowledge of NVIDIA GPU telemetry, CUDA programming, and performance optimization for AI workloads Understanding of AI ethics, data governance, and regulatory frameworks for machine learning systems Why Join Capital One India Work at the intersection of technology, AI, and compliance helping shape the future of responsible AI Join a team driving enterprise-wide adoption of Generative AI Collaborate with world-class engineers, data scientists, and product leaders Enjoy a high-performance culture that encourages innovation, learning, and mentorship Access to cutting-edge tools, open-source contributions, and cloud-native infrastructure Qualification : Bachelors or Masters degree in Computer Science, Engineering, or related field

Role: Senior Analyst Machine Learning Performance & Testing Location: Bengaluru, Karnataka, India Experience: 3 5 Years Employment Type: Permanent, Full-Time About the Role We are seeking a skilled and detail-oriented Senior Analyst with strong experience in ML model performance testing, load testing, and end-to-end (E2E) automation. This role is focused on ensuring scalable, low-latency deployment of production-grade machine learning models. The ideal candidate will be proficient in evaluating model performance under varied workloads, building robust test frameworks, and enhancing system monitoring. Key Responsibilities Conduct load testing and performance benchmarking for machine learning models under varying requests per second (RPS) scenarios. Develop and automate end-to-end test cases to validate model readiness and support smooth rollouts. Monitor and improve model scalability, response time, and error rates across production environments. Collaborate with ML engineers, backend developers, and QA test teams to ensure seamless integration and testing workflows. Identify and address bottlenecks in model inference, helping improve performance for high-volume, low-latency applications. Set up alerting and observability pipelines for model health using industry-standard tools. Required Skills & Tools Performance Testing & Monitoring: ML Load Testing, Job Monitoring, Model Scalability Evaluation Platforms & Tools: Databricks, MLflow, Seldon, Kubeflow, Tecton, Jenkins Cloud Services: Experience with AWS and deploying/testing models in cloud environments Programming Languages: Proficiency in at least one of the following Python, Java, Scala Experience: Working with production-level ML models, especially involving high data volumes and real-time inference Strong communication skills and ability to work in cross-functional teams Preferred Qualifications Hands-on experience with CI/CD pipelines for ML systems Knowledge of A/B testing and canary deployments for ML models Experience building testing frameworks for ML infrastructure at scale Understanding of monitoring and alerting best practices in production ML systems Be at the forefront of ML operations and model performance optimization Collaborate with industry-leading engineers and contribute to cutting-edge AI deployments Gain deep exposure to real-time data systems, cloud platforms, and enterprise-scale ML testing Competitive compensation and an innovative, fast-paced work environment

Machine Learning Engineer Full-Time - Bengaluru, India - Data Science / Artificial Intelligence / Engineering Join our dynamic Data Science / Artificial Intelligence / Engineering team in Bengaluru, India as a Full-Time Machine Learning Engineer and play a key role in driving data-driven innovation! We are seeking a skilled and results-oriented Machine Learning Engineer to design, build, and deploy scalable machine learning models that address real-world business challenges. You will collaborate closely with data scientists, engineers, and product managers to transform raw data into actionable insights and integrate intelligent features into our products. As a Machine Learning Engineer, you will be responsible for the complete lifecycle of machine learning models and pipelines, from design and development to seamless deployment for a variety of applications. This includes classification, regression, clustering, recommendation systems, and time-series forecasting. You will leverage your expertise to preprocess and analyze large and complex datasets, extracting meaningful features and valuable insights. Collaboration with cross-functional teams will be crucial as you identify strategic ML opportunities and define clear success metrics. A key aspect of this role involves optimizing machine learning models for peak performance, scalability, and accuracy within production environments. You will build robust APIs or efficient microservices to integrate these models seamlessly into our applications, utilizing tools such as Flask or FastAPI. Continuous improvement is paramount, and you will be responsible for the ongoing monitoring and retraining of models based on their performance and any signs of data drift. Staying at the forefront of the field is essential, and you will be expected to stay updated with the latest ML research and emerging technologies, applying them to continuously enhance our product capabilities. Key Responsibilities: Design, develop, and deploy machine learning models and pipelines for diverse applications including classification, regression, clustering, recommendation, and time-series forecasting. Preprocess and analyze large datasets to extract meaningful features and actionable insights. Collaborate effectively with cross-functional teams to identify strategic ML opportunities and define clear success metrics. Optimize models for maximum performance, scalability, and accuracy in production environments. Build robust APIs or efficient microservices to integrate ML models into applications using tools like Flask or FastAPI. Continuously monitor and retrain models based on performance metrics and potential data drift. Stay updated with the latest ML research and technologies and apply them to enhance product capabilities. Minimum Qualifications: Bachelor s or Master s degree in Computer Science, Data Science, Statistics, or a related field. 2+ years of proven experience as a Machine Learning Engineer or in a similar role. Strong proficiency in Python and key ML libraries such as Scikit-learn, XGBoost, TensorFlow, or PyTorch. Practical experience working with both SQL and NoSQL databases. Solid knowledge of essential data preprocessing, effective feature engineering, and robust model evaluation techniques. Familiarity with standard software engineering practices, including version control (Git), thorough code reviews, and efficient CI/CD pipelines. Preferred Qualifications: Prior experience with deep learning, natural language processing (NLP), or computer vision. Familiarity with major cloud services like AWS, GCP, or Azure (especially SageMaker, Vertex AI, etc.). Understanding of modern MLOps tools and practices (e.g., MLflow, Kubeflow, DVC). Practical experience with containerization and orchestration tools (Docker, Kubernetes). Knowledge of big data tools (e.g., Spark, Hadoop) is considered a significant plus. What We Offer: Competitive salary and performance-based incentives to reward your contributions. Comprehensive health insurance and valuable wellness benefits to support your well-being. Dedicated learning and development programs for continuous professional growth. Exciting opportunities to work on impactful, real-world AI/ML projects with significant scale. A collaborative, inclusive, and innovative work culture that fosters teamwork and creativity. Flexible working hours and a hybrid work model to promote a healthy work-life balance.

Job Title: Cloud-Based AI Developer - Advanced Analytics Group (AAG) Company: Bain & Company Job Type: Full-Time Employment Type: Permanent What Makes Us a Great Place to Work: We are proud to be consistently recognized as one of the world s best places to work, a champion of diversity, and a model of social responsibility. We are currently ranked #1 on Glassdoor's Best Places to Work list, and we have maintained a spot in the top four for the last 13 years. Diversity, inclusion, and collaboration are key to building extraordinary teams. We hire people with exceptional talents, abilities, and potential, creating an environment where you can thrive both professionally and personally. We are publicly recognized for being a great place to work for diversity and inclusion, women, LGBTQ, and parents. Who You ll Work With: As a member of Bain s Advanced Analytics Group (AAG), you will work alongside generalist consultants to help clients across industries solve their biggest problems using expertise in data science, customer insights, statistics, machine learning, data management, supply chain analytics, and data engineering. AAG team members hold advanced degrees in computer science, engineering, AI, data science, physics, statistics, mathematics, and other quantitative disciplines, with backgrounds in tech, data science, marketing analytics, and academia. We are committed to building a diverse and inclusive team and encourage candidates of all backgrounds to apply. What You ll Do: As a member of the AAG, you will be responsible for designing, developing, and maintaining cloud-based AI applications that provide high-quality, scalable, and secure solutions for our clients. Your work will encompass the full stack, from API design to deployment, delivering analytics solutions across various sectors. Cloud-Based AI Development: Design, develop, and maintain cloud-based AI applications, ensuring scalability and security, leveraging full-stack technology solutions. Cross-Functional Collaboration: Work with product managers, data scientists, and other engineers to define and implement analytics features that meet business requirements. Cloud and Containerization: Use Kubernetes and containerization technologies to deploy, manage, and scale applications in cloud environments for optimal performance. API & Microservices Development: Develop and maintain APIs and microservices to expose analytics functionality, adhering to industry best practices for design and documentation. Security and Compliance: Implement robust security measures to protect sensitive data and ensure compliance with data privacy regulations. Troubleshooting and Performance Monitoring: Continuously monitor and troubleshoot application performance, resolving issues impacting system reliability and user experience. Code Reviews and Best Practices: Participate in code reviews and contribute to the establishment of coding standards to ensure high-quality, maintainable code. Emerging Trends and Technologies: Stay current with emerging trends in cloud computing, data analytics, and software engineering to enhance the platform s capabilities. Collaboration with DevOps: Work with DevOps and infrastructure teams to automate deployment and release processes, optimizing the development workflow. Client Collaboration: Collaborate closely with business consulting teams to assess opportunities and develop analytics solutions across sectors. Education and Influence: Influence and educate clients on analytics application engineering capabilities, supporting their teams directly. Travel: Expect occasional travel (30%) for project work. About You: Required Qualifications: Education: Master s degree in Computer Science, Engineering, or a related technical field. Experience: 3+ years of experience at Senior or Staff level, or equivalent. Expertise in client-side technologies such as React, Angular, Vue.js, HTML, and CSS. Experience with server-side technologies such as Django, Flask, and Fast API. Proficiency with cloud platforms (AWS, Azure, GCP) and Terraform automation (good to have). 3+ years of expertise in Python. Experience using Git for version control and collaboration. Familiarity with DevOps, CI/CD, and tools like GitHub Actions. Demonstrated interest in LLMs, prompt engineering, and Langchain. Experience with workflow orchestration tools such as dbt, Beam, Airflow, Luigi, Metaflow, Kubeflow, or similar. Experience in the implementation of large-scale structured or unstructured databases, as well as containerization technologies like Docker and Kubernetes. Skills and Knowledge: Strong interpersonal and communication skills to explain complex engineering topics to colleagues and clients from various disciplines. Curiosity, proactivity, and critical thinking. Solid computer science fundamentals in data structures, algorithms, automated testing, object-oriented programming, performance complexity, and software architecture. Expertise in designing API interfaces and knowledge of data architecture and database schema design. Familiarity with agile development methodologies. Join Bain & Company: Become a part of a forward-thinking team committed to solving complex problems, building innovative solutions, and delivering impactful data analytics and AI solutions. Collaborate with talented professionals and gain valuable experience that shapes the future of data analytics and AI. Qualification : Masters degree in Computer Science, Engineering, or a related technical field.