Description

Required Qualifications

• Build GenAI applications leveraging foundation models and advanced architectures such as GraphRAG.
• Develop autonomous AI agents using modern agentic frameworks.
• Design and deploy RAG and GenAI services using Python (FastAPI), Docker, and cloud platforms (AWS, Azure, or GCP).
• Build scalable REST APIs that power LLM-driven applications integrated with enterprise data sources.
• Implement LLM evaluation frameworks using tools such as Ragas, LangSmith, or custom benchmarks to measure answer relevance, groundedness, and hallucination rates.
• Apply LLMOps/MLOps practices, including CI/CD pipelines, prompt/version management, automated testing, and monitoring of latency, cost, and response quality.
• Develop systems leveraging embeddings at scale, knowledge graphs, and ontology extraction.
• Collaborate across engineering teams, mentor developers, and help drive innovation in GraphRAG and agentic AI architectures

Key Skills

Education

Any Gradute