GEN AI Engineer

Design and implement GenAI solutions using LLMs for text, code, and knowledge-based use cases. Develop prompt engineering, prompt orchestration, and reusable prompt libraries. Fine-tune and evaluate LLMs for enterprise-specific tasks. Integrate GenAI capabilities into applications, APIs, and workflows. Agentic AI & Autonomous Systems Design and implement agentic architectures (planner, executor, evaluator, memory). Build multi-agent systems with task decomposition, tool usage, and feedback loops. Enable agents to interact with enterprise tools, APIs, databases, and knowledge stores. Implement agent memory, reasoning chains, and self-reflection mechanisms. Architecture & Engineering Define end-to-end AI solution architecture (cloud, APIs, security, observability). Implement Retrieval-Augmented Generation (RAG) with vector databases. Ensure scalability, performance, reliability, and cost efficiency. Apply Responsible AI, governance, and compliance standards. Collaboration & Delivery Work closely with product owners, domain experts, and engineering teams. Translate business problems into AI-driven solutions.

Technical Skills Strong experience with Python (mandatory); exposure to Java/TypeScript is a plus. Hands-on experience with LLMs and GenAI frameworks (e.g., LangChain-style orchestration, agent frameworks). Experience with Agentic AI concepts: planning, tool use, memory, multi-agent coordination. Experience with vector databases and semantic search. API integration and microservices architecture. Cloud platforms (Azure / AWS / GCP). AI/ML Knowledge Understanding of NLP, deep learning, and transformer-based models. Experience with model evaluation, hallucination mitigation, and prompt optimization. Familiarity with Responsible AI, security, and data privacy principles.