Lead ML Engineer

About Info Edge India Ltd. :

InfoEdge’s mission is to create world-class platforms that transform lives by continuously innovating. Our products and services are built keeping our customers in mind. We always delight our customers by delivering superior value through enhanced offerings on the internet and other platforms. Through our continuous investment across various businesses, especially in cutting-edge technology, machine learning and artificial intelligence (AI), we have built a robust system that constantly increases our predictive powers on customer behaviour, and optimizes and improves our systems. Our various teams tirelessly work together to solve problems, innovate, and create something to empower our customers. 

At Info Edge, people are our core competitive advantage and we will continue doing all that is needed to attract and retain the best available talent. 

Team: AI Team – Central Function 

Title: Lead ML Engineer

Required Educational Qualification: BE/B.Tech in Computer Science/IT/ Electronicsfrom reputed Institute

Desired Experience: 3-7 Years

As a Lead AI Engineer you will: 

• Design and develop a centralized ML platform, including feature store, model registry, experiment tracking, and serving layer. 
• Design and own model serving infrastructure, including real-time and batch inference pipelines with SLA-grade reliability. 
• Evaluate and implement inference optimization strategies, including quantization, tensor parallelism, vLLM/Triton-based serving, and TensorRT compilation. 
• Develop GenAI applicationsleveraging LLMs, RAG, tool calling, agentic workflows, and knowledge retrieval systems. 
• Establish MLOps best practices, including CI/CD for ML, model versioning, drift detection, and automated retraining pipelines. 
• Design and maintain automated load-testing pipelinesto benchmark model performance under peak and spike traffic conditions. 
• Implement advanced deployment strategiessuch asshadow testing to evaluate new model versions on live traffic without impacting user experience. 
• Partner with Data Scientiststo productionize models by translating prototype code into scalable, maintainable production services and providing actionable feedback on infrastructure and engineering constraints.  
• Collaborate with Platform, DevOps, and Product Engineering teamsto embed ML capabilitiesinto the product stack.