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 deploymentsKey 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).
