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 robust 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).

📌 Sr Engineer - AI COE (Bengaluru)
🏢 Tata Communications
📍 Bengaluru