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 (Data Scientist/ML Engineer)
Role Summary:
We are hiring a Sr. Engineer (2+ years) for the AI CoE to build and deploy Generative AI, RAG, Agentic AI, and Machine Learning 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
- 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 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: 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
- 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 (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 ( 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).
