Sr Engineer - AI COE

Tata Communications; Bengaluru; On-site; INR 1,200,000 - 2,500,000; Full time

Job summary

Tata Communications Limited in Bengaluru, India is seeking a Sr. Engineer to build and deploy Generative AI, RAG, and data engineering solutions within the AI CoE. This hands-on role focuses on LLM-driven applications, model evaluation, and scalable backend services.

You will design multi-agent workflows, implement vector search pipelines, and containerized deployments, collaborating with product and engineering teams to deliver production-grade AI solutions.

Qualifications

• 2+ years of experience in Data Engineering / AI / Machine Learning / Gen AI.
• Excellent proficiency in Python and working knowledge of SQL (PostgreSQL preferred).
• Hands-on Data Engineering with big data tools and technology.
• Hands-on experience with ML algorithms and model development.
• Experience with LLMs, Generative AI, RAG architectures, and Agentic workflows.

Responsibilities

• Build and deploy LLM-based applications, RAG, and Agentic RAG systems.
• Build optimized data pipelines to support AI applications.
• Develop multi-agent workflows using LangChain, LlamaIndex, LangGraph, Azure, and AWS Bedrock.
• Integrate MCP tools, APIs, and function-calling into AI systems.
• Design prompting strategies, embeddings, and vector search pipelines using Milvus / FAISS / Pinecone.
• Implement structured output generation for LLM responses (JSON/schema-driven outputs).
• Apply ML techniques for telecom and enterprise use cases; data preprocessing and feature engineering.
• Develop backend APIs using FastAPI for serving AI/ML models.
• Containerize applications using Docker for scalable deployment.

Skills

Python

SQL

Data Engineering

Machine Learning

Generative AI

LLMs

LangChain

FastAPI

Docker

Cloud (Azure/AWS/GCP)

Tools

Milvus

FAISS

Pinecone

LangChain

LlamaIndex

LangGraph

PostgreSQL

Job description

About The Company

Tata Communications Redefines Connectivity with Innovation and IntelligenceDriving the next level of intelligence powered by Cloud, Mobility, Internet of Things, Collaboration, Security, Media services and Network services, we at Tata Communications are envisaging a New World of Communications

Job Title

Sr. Engineer (ML Engineer/Data Engineer)

Role Summary

We are hiring a Sr. Engineer (2+ years) for the AI CoE to build and deploy Generative AI, RAG, Agentic AI, Machine Learning & Data Engineering solutions. This role combines LLM-driven applications with strong ML fundamentals, supporting scalable and monetizable AI products for telecom and enterprise use cases.

Key Responsibilities

• Build and deploy LLM-based applications, RAG, and Agentic RAG systems
• Build optimized data pipeline to support AI applications
• Develop multi-agent workflows using tools like LangChain, LlamaIndex, LangGraph, Azure, and AWS Bedrock
• Integrate MCP tools, APIs, and function-calling into AI systems
• Design prompting strategies, embeddings, and vector search pipelines using Milvus / FAISS / Pinecone
• Implement structured output generation for LLM responses (JSON/schema-driven outputs)
• Apply machine learning techniques (classification, clustering, predictive modeling) for telecom and enterprise use cases
• Perform data preprocessing, feature engineering, and model evaluation
• Optimize solutions for performance, scalability, cost, and latency
• Develop backend APIs using FastAPI for serving AI/ML models
• Containerize applications using Docker for scalable deployment
• Implement monitoring, logging, and observability using Grafana and ELK stack
• Enable LLM traceability and observability using Langfuse etc.
• Collaborate with product and engineering teams to deliver production-grade AI solutions Required Skills:
• 2+ years of experience in Data Engineering / AI / Machine Learning /Gen AI
• Excellent proficiency in Python and working knowledge of SQL (PostgreSQL preferred)
• Hands on Data Engineering experience with big data tools and technology.
• Hands-on experience with Machine Learning algorithms and model development
• Strong understanding of ML concepts: supervised/unsupervised learning, feature engineering, model evaluation, bias-variance tradeoff
• Hands-on experience with LLMs, Generative AI, RAG architectures, and Agentic workflows
• Experience with LangChain / LlamaIndex / LangGraph / vector databases (Milvus, FAISS, Pinecone)
• Experience building APIs using FastAPI
• Understanding of structured output handling in LLMs
• 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 (LLM tracing & monitoring)
• Familiarity with Grafana, ELK stack for monitoring
• Experience with Docker-based deployments Key Focus Areas:
• Agent-based chatbots & virtual assistants
• Data pipelines
• RAG-based knowledge systems
• Multi-agent automation workflows (LangGraph-based)
• ML-driven use cases (prediction, recommendation, analytics) in telecom
• Scalable AI APIs and microservices using FastAPI

Additional Note

Role involves hands-on Python coding and system design assessments, covering machine learning fundamentals, model building, RAG, LLMs, agent-based applications, LangGraph workflows, API development, and production deployment considerations (Docker, monitoring, traceability).