Senior Engineer - Data Science/ML Engineer

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).