Description

Required Qualifications

• 7+ years in software engineering or applied ML building real-world AI/ML systems; strong Python proficiency and backend development expertise.
• Hands-on experience building GenAI apps with LangChain and LangGraph, including agent design, state/memory management, and graph-based orchestration.
• Proficiency in ML/NLP and generative models; experience with embeddings, vector stores, RAG, and LLM integration/fine-tuning (OpenAI, LLaMA, Cohere, etc.)
• Strong coding in Python and experience with frameworks/tools such as FastAPI, PyTorch/TensorFlow, MLflow; solid understanding of software engineering fundamentals and secure development.
• Experience with AI agent frameworks and MCP; familiarity with agent observability (LangSmith/LangFuse) and agentic RAG patterns
• Track record of delivering scalable, production AI systems and collaborating across teams.
• Experience with agent frameworks (AutoGen, CrewAI), tool-use ecosystems, and advanced planning/reasoning strategies
• Knowledge of cloud platforms (AWS), MLOps, and data pipelines; React.js familiarity is a plus.
• Exposure to enterprise environments and secure, compliant deployments

Key Skills

• Programming: Python; backend APIs (FastAPI)
• AI/ML: ML/NLP, generative AI, embeddings, model evaluation
• Frameworks: LangChain, LangGraph; plus LlamaIndex, PyTorch, TensorFlow, MLflow
• Architectures: RAG, Transformers, OCR
• Agents: Design and orchestration, memory/state management, tool integration; MCP and agent-to-agent protocols
• Observability: LangSmith/LangFuse for agent monitoring

Key Skills

Education

Any Graduate