Sr Engineer - AI COE

As a Sr. Engineer (Data Scientist/ML Engineer) at Tata Communications, your role will involve building and deploying Generative AI, RAG, Agentic AI, and Machine Learning solutions for telecom and enterprise use cases. You will be part of the AI CoE team, leveraging LLM-driven applications and ML fundamentals to create scalable and monetizable AI products. Your responsibilities will include: - Building and deploying LLM-based applications, RAG, and Agentic RAG systems - Developing multi-agent workflows using tools like LangChain, LlamaIndex, LangGraph, Azure, and AWS Bedrock - Integrating MCP tools, APIs, and function-calling into AI systems - Designing prompting strategies, embeddings, and vector search pipelines using Milvus / FAISS / Pinecone - Implementing structured output generation for LLM responses (JSON/schema-driven outputs) - Applying machine learning techniques for telecom and enterprise use cases - Performing data preprocessing, feature engineering, and model evaluation - Optimizing solutions for performance, scalability, cost, and latency - Developing backend APIs using FastAPI for serving AI/ML models - Containerizing applications using Docker for scalable deployment - Implementing monitoring, logging, and observability using Grafana and ELK stack - Enabling LLM traceability and observability using Langfuse - Collaborating with product and engineering teams to deliver production-grade AI solutions Required Skills: - 2+ years of experience in Data Science / AI / Machine Learning /Gen AI - Excellent proficiency in Python and working knowledge of SQL (PostgreSQL preferred) - Hands-on experience with Machine Learning algorithms and model development - Strong understanding of ML concepts - Hands-on experience with LLMs, Generative AI, RAG architectures, and Agentic workflows - Experience with LangChain / LlamaIndex / LangGraph / vector databases - Experience building APIs using FastAPI - Understanding of structured output handling in LLMs - Solid foundation in statistics and data analysis Good to Have: - Exposure to MCP tools and LLM orchestration frameworks - Experience with Agentic AI / multi-agent systems - Cloud experience (Azure / AWS / GCP) - Experience with Langfuse for LLM tracing & monitoring - Familiarity with Grafana, ELK stack for monitoring - Experience with Docker-based deployments Key Focus Areas: - Agent-based chatbots & virtual assistants - RAG-based knowledge systems - Multi-agent automation workflows - ML-driven use cases in telecom - Scalable AI APIs and microservices using FastAPI Please note that this role will involve hands-on Python coding, system design assessments, and covering various aspects of machine learning fundamentals, model building, RAG, LLMs, agent-based applications, LangGraph workflows, API development, and production deployment considerations. As a Sr. Engineer (Data Scientist/ML Engineer) at Tata Communications, your role will involve building and deploying Generative AI, RAG, Agentic AI, and Machine Learning solutions for telecom and enterprise use cases. You will be part of the AI CoE team, leveraging LLM-driven applications and ML fundamentals to create scalable and monetizable AI products. Your responsibilities will include: - Building and deploying LLM-based applications, RAG, and Agentic RAG systems - Developing multi-agent workflows using tools like LangChain, LlamaIndex, LangGraph, Azure, and AWS Bedrock - Integrating MCP tools, APIs, and function-calling into AI systems - Designing prompting strategies, embeddings, and vector search pipelines using Milvus / FAISS / Pinecone - Implementing structured output generation for LLM responses (JSON/schema-driven outputs) - Applying machine learning techniques for telecom and enterprise use cases - Performing data preprocessing, feature engineering, and model evaluation - Optimizing solutions for performance, scalability, cost, and latency - Developing backend APIs using FastAPI for serving AI/ML models - Containerizing applications using Docker for scalable deployment - Implementing monitoring, logging, and observability using Grafana and ELK stack - Enabling LLM traceability and observability using Langfuse - Collaborating with product and engineering teams to deliver production-grade AI solutions Required Skills: - 2+ years of experience in Data Science / AI / Machine Learning /Gen AI - Excellent proficiency in Python and working knowledge of SQL (PostgreSQL preferred) - Hands-on experience with Machine Learning algorithms and model development - Strong understanding of ML concepts - Hands-on experience with LLMs, Generative AI, RAG architectures, and Agentic workflows - Experience with LangChain / LlamaIndex / LangGraph / vector databases - Experience building APIs using FastAPI - Understanding of structured output handling in LLMs - Solid foundation in statistics and data analysis Good to Have: - Exposure to MCP tools and LLM orchestration frameworks - Experience with Agentic AI / multi-agent sy