Top Machine Learning Jobs in India

Lead a team of AI QA Engineers, driving QA strategy for ML models, develop automated test frameworks, validate APIs, and ensure high quality in AI solutions.

The role involves developing scalable AI/ML models, collaborating with cross-functional teams, maintaining production solutions, and driving experimentation in AI/ML techniques.

Lead initiatives to enhance the ML platform, mentor junior engineers, build scalable ML models, and ensure code quality while driving innovation in a startup environment.

The Machine Learning Engineer will develop and innovate ML models and their infrastructure, collaborate, mentor, and take ownership of impactful projects.

Lead and grow Gracenote's AI/ML and Data Science teams, establish technical rigor and operating cadence, manage cross-functional stakeholder delivery, oversee ML/GenAI/NLP/computer vision research and production deployments, drive MLOps and QA, automate recurring analyses, and evolve predictive analytics and market-research methodologies for media measurement.

The Associate Systems Developer will support application development and maintenance activities, requiring programming in C#, and working with .NET technologies and Azure DevOps.

The Senior Machine Learning Engineer will lead the architecture and implementation of a generative AI-powered video creative platform, focusing on optimizing video ad creation while collaborating with multiple teams.

Responsible for building and optimizing ML microservices, writing production code in Python, maintaining APIs, and collaborating with teams to deploy ML features.

Design, build, deploy, and monitor LLM-based GenAI applications and agentic systems. Implement RAG pipelines, vector DB integrations, AI backends (FastAPI/Flask), chat UIs, and cloud MLOps ensuring performance, scalability, security, and Responsible AI compliance.

Design and develop end-to-end AI capabilities in NLP, optimize ML models and systems, and collaborate with cross-functional teams.

As an AI/ML Engineer, you will develop machine learning models, collaborate with teams, preprocess data, and optimize ML pipelines. Additionally, you will mentor junior members and contribute to AI/ML strategies.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Responsible for architecting and deploying AI computer vision solutions. Collaborates with teams to implement ML solutions, improves processes, and engages with business units to drive innovation.

The ML Ops Engineer will design, implement, and manage ML pipelines, deploy models in production, and utilize Databricks for data engineering while collaborating with cross-functional teams.

The Associate Architect - Machine Learning role involves developing cloud ML solutions on AWS, fine-tuning LLMs, and collaborating with teams for optimal model implementation.

The role involves designing and implementing production ML systems, mentoring junior engineers, and establishing best practices for ML performance and evaluation.

The role involves building and deploying ML solutions, data analysis, model development, and collaborating with stakeholders, focusing on business impact.

The AI/ML Engineer will develop generative AI models, deploy ML models, write scalable code, and work with various AI technologies and frameworks.

The Head of Machine Learning will manage a high-performing ML team, ensure the success of ML systems, and drive initiatives across business partnerships. The role emphasizes strong production engineering and theoretical ML expertise.

Develop and optimize machine learning models, manage project priorities, and improve real-world solutions using advanced ML techniques.

Lead design and production of computer vision biometric systems (face detection, quality, attributes, recognition). Build/train models, implement fairness benchmarking, own end-to-end ML pipelines on AWS, optimize for low-latency inference, and mentor engineers.

Lead the analytics for Trading Partner Network, developing dashboards and insights while optimizing processes and ensuring revenue capture across partnerships.

Responsible for designing, training, and deploying machine learning models, leading teams, conducting experiments, and documenting processes for AI solutions.

As a Machine Learning Researcher, you'll lead research in applying ML to trading problems, develop deep learning models, and collaborate with quant researchers.

Research, prototype, and implement generative and ML algorithms; design, develop, deploy, and maintain Models-as-a-Service; lead MLOps best practices and mentor engineers; collaborate across Data, Cloud, and Architecture teams to deliver model deployments on AWS and GCP.