Senior Engineer - AI COE

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).