{"id":3627712,"url":"https://alion.io/job/tata-communications-sr-engineer-ai-coe","title":"Sr Engineer - AI COE","company":{"id":180492,"name":"Tata Communications","domain":"tatacommunications.com","url":"https://alion.io/company/tata-communications","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Freshteam","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Grafana","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Milvus","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Pinecone","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"GCP","optional":true}],"status":"live","first_seen_at":"2026-11-02T18:46:14Z","employer_posted_date":null,"last_verified_at":"2026-11-02T18:46:14Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Job Title:\nSr. Engineer (ML Engineer/Data Engineer)\nRole Summary:\nWe 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.\nKey Responsibilities:\nBuild and deploy LLM-based applications, RAG, and Agentic RAG systems\n\nBuild optimized data pipeline to support AI applications\nDevelop multi-agent workflows using tools like LangChain, LlamaIndex, LangGraph, Azure, and AWS Bedrock\n\nIntegrate MCP tools, APIs, and function-calling into AI systems\n\nDesign prompting strategies, embeddings, and vector search pipelines using Milvus / FAISS / Pinecone\n\nImplement structured output generation for LLM responses (JSON/schema-driven outputs)\n\nApply machine learning techniques (classification, clustering, predictive modeling) for telecom and enterprise use cases\n\nPerform data preprocessing, feature engineering, and model evaluation\n\nOptimize solutions for performance, scalability, cost, and latency\n\nDevelop backend APIs using FastAPI for serving AI/ML models\n\nContainerize applications using Docker for scalable deployment\n\nImplement monitoring, logging, and observability using Grafana and ELK stack\n\nEnable LLM traceability and observability using Langfuse etc.\n\nCollaborate with product and engineering teams to deliver production-grade AI solutionsRequired Skills:\n\n2+ years of experience in Data Engineering / AI / Machine Learning /Gen AI\n\nExcellent proficiency in Python and working knowledge of SQL (PostgreSQL preferred)\n\nHands on Data Engineering experience with big data tools and technology.\nHands-on experience with Machine Learning algorithms and model development\n\nStrong understanding of ML concepts: supervised/unsupervised learning, feature engineering, model evaluation, bias-variance tradeoff\n\nHands-on experience with LLMs, Generative AI, RAG architectures, and Agentic workflows\n\nExperience with LangChain / LlamaIndex / LangGraph / vector databases (Milvus, FAISS, Pinecone)\n\nExperience building APIs using FastAPI\n\nUnderstanding of structured output handling in LLMs\n\nGood to Have:\n\nExposure to MCP tools and LLM orchestration frameworks\n\nExperience with Agentic AI / multi-agent systems\n\nCloud experience (Azure / AWS / GCP)\n\nExperience with Langfuse (LLM tracing & monitoring)\n\nFamiliarity with Grafana, ELK stack for monitoring\n\nExperience with Docker-based deploymentsKey Focus Areas:\n\nAgent-based chatbots & virtual assistants\n\nData pipelines\nRAG-based knowledge systems\n\nMulti-agent automation workflows (LangGraph-based)\n\nML-driven use cases (prediction, recommendation, analytics) in telecom\n\nScalable AI APIs and microservices using FastAPIAdditional Note:\nRole 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, 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