IN_Manager_GenAI+AgenticAI+Data Science_D&A_Advisory_Bangalore

Job Description & Summary:  

We're looking for a Senior AI/ML Engineer who can design, build, and deploy scalable ML, GenAI, and Agentic AI systems across cloud environments (GCP preferred) with strong focus on productionization, automation, and business impact. You'll work across demand forecasting, RAG-based intelligent applications, autonomous multi-agent systems, and enterprise AI integration. 

Responsibilities: 

• 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 

Mandatory 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 

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 

Years of experience required: 

8-12 years 

Education qualification: 

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (60% above)