Senior Engineer

We are looking for a skilled Sr. Engineer with 2+ years of experience in Data Science, Machine Learning, and Generative AI to join our AI Center of Excellence (AI CoE).

The role involves designing, developing, and deploying Generative AI, RAG, Agentic AI, and Machine Learning solutions. The candidate will work on building scalable, production-ready AI applications and intelligent automation solutions for telecom and enterprise use cases.

Key Responsibilities:

• Build and deploy LLM-based applications, RAG, and Agentic RAG systems.
• Develop multi-agent workflows using LangChain, LlamaIndex, LangGraph, Azure, and AWS Bedrock.
• Integrate MCP tools, APIs, and function calling into AI applications.
• Design prompting strategies, embeddings, and vector search pipelines using Milvus, FAISS, and Pinecone.
• Implement structured output generation using JSON and schema-driven approaches.
• Apply ML techniques including classification, clustering, and predictive modeling.
• Perform data preprocessing, feature engineering, and model evaluation.
• Optimize AI solutions for performance, scalability, cost, and latency.
• Develop backend APIs using FastAPI.
• Containerize applications using Docker.
• Implement monitoring, logging, and observability using Grafana and ELK Stack.
• Enable LLM tracing and observability using Langfuse and similar tools.
• Collaborate with product and engineering teams to deliver production-grade AI solutions.

Required Skills:

• 2+ years of experience in Data Science, AI, Machine Learning, or Generative 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 supervised and unsupervised learning, feature engineering, model evaluation, and bias-variance tradeoff.
• Hands-on experience with LLMs, Generative AI, RAG architectures, and Agentic workflows.
• Experience with LangChain, LlamaIndex, LangGraph, and vector databases (Milvus, FAISS, Pinecone).
• Experience building APIs using FastAPI.
• Understanding of structured output handling in LLMs.
• Strong foundation in statistics and data analysis.

Good to Have:

• Exposure to MCP tools and LLM orchestration frameworks.
• Experience with Agentic AI and multi-agent systems.
• Cloud experience with Azure, AWS, or GCP.
• Experience with Langfuse for LLM tracing and monitoring.
• Familiarity with Grafana and ELK Stack.
• Experience with Docker-based deployments.

Key Focus Areas:

• Agent-based chatbots and virtual assistants.
• RAG-based knowledge management systems.
• Multi-agent automation workflows using LangGraph.
• ML-driven prediction, recommendation, and analytics use cases in telecom.
• Scalable AI APIs and microservices using FastAPI.