Senior Associate – AI/ML Engineer, Advisory

Overview

Senior AI/ML Engineer role designing, building, and deploying scalable ML, GenAI, and Agentic AI systems across cloud environments, with a strong focus on productionization, automation, and business impact.

What you'll do

• Build end-to-end ML/AI pipelines (data → model → deployment → monitoring)
• Develop and deploy ML, Deep Learning, NLP, and GenAI models in production
• Design and implement RAG systems — retrieval, chunking, embeddings, vector search, and prompt engineering
• Build Agentic AI solutions — autonomous agents, multi-agent workflows, tool-calling, planning, and memory
• Build and optimize time series forecasting models (demand forecasting, inventory planning)
• Implement MLOps pipelines — CI/CD, model monitoring, drift detection, governance
• Optimize models for performance, cost, and latency
• Integrate AI systems with enterprise APIs, data platforms, and customer-facing applications
• Design scalable LLM inference architectures for efficient deployment
• Collaborate with data scientists, product managers, engineers, and business stakeholders in Agile teams
• Debug, optimize, and enhance ML models for quality and performance improvements
• Mentor team members and present technical findings to diverse audiences
• Stay current with AI/GenAI trends and evaluate emerging tools and frameworks

What you'll need

• Bachelor's or Master's degree in Computer Science, Engineering, or related field (60% above)
• Degrees/Field of Study required: MBA (Master of Business Administration), Bachelor of Engineering
• 4-12 years of experience
• Required skills: Java Authentication and Authorization Service (JAAS), Java Spring

Nice to have

• Preferred skill sets: Build end-to-end ML/AI pipelines (data → model → deployment → monitoring); Develop and deploy ML, Deep Learning, NLP, and GenAI models in production; Design and implement RAG systems — retrieval, chunking, embeddings, vector search, and prompt engineering; Build Agentic AI solutions — autonomous agents, multi-agent workflows, tool-calling, planning, and memory; Build and optimize time series forecasting models (demand forecasting, inventory planning); Implement MLOps pipelines — CI/CD, model monitoring, drift detection, governance; Optimize models for performance, cost, and latency; Integrate AI systems with enterprise APIs, data platforms, and customer-facing applications; Design scalable LLM inference architectures for efficient deployment; Collaborate with data scientists, product managers, engineers, and business stakeholders in Agile teams; Debug, optimize, and enhance ML models for quality and performance improvements; Mentor team members and present technical findings to diverse audiences; Stay current with AI/GenAI trends and evaluate emerging tools and frameworks

Details

• Location: Gurugram Downtown 4
• Visa sponsorship: No
• Government clearance required: No
• Job posting end date: July 20, 2026