Sr Engineer - AI COE

Tata Communications · Artificial Intelligence · Unified Communications · Python · SQL · 1 day ago
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Location
In office (Bengaluru)
Seniority
Senior
Employment
Full-Time

First seen by Alion on Nov 2, 2026.

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Tata Communications

Tata Communications is an Indian digital infrastructure company that operates a global network. It provides subsea cable capacity, cloud connectivity, IoT and collaboration services. The company carries a significant share of world internet traffic.
tatacommunications.com · HQ: Bengaluru, Mumbai, India · Unified Communications · Network Interconnect · Telecom Infrastructure · 51-200 employees · Est. 1986

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).

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