AI Engineer

AI Solution Design & Development

• Design, prototype, and validate AI-powered features spanning Generative AI, NLP, and Agentic AI use cases.
• Train, fine-tune, and evaluate language or vision models where pre-built or hosted models are insufficient — and operationalize them for inference in production.
• Architect and deliver production-ready, large-document advanced RAG workflows, including chunking strategies, hybrid retrieval, re-ranking, and evaluation.
• Build complex multi-agent systems — designing reusable, composable agent capabilities (skills, tools, actions) that can be dynamically invoked by LLMs.
• Implement agent interoperability and orchestration using protocols such as Agent-to-Agent (A2A), Agent Communication Protocol (ACP), and Model Context Protocol (MCP).
• Develop modular, reusable Python APIs and reference implementations for use cases including chatbots, document Q&A, summarization, and intelligent automation.
• Apply prompt engineering, context engineering, and solution tuning to optimize accuracy, latency, and cost.

Deployment & Optimization

• Provide well-documented proof-of-concepts and reference implementations to Full Stack and DevOps teams for integration and deployment.
• Collaborate with backend and cloud engineers to ensure AI solutions meet performance, cost, and security constraints.
• Build and optimize inference pipelines; monitor token usage, latency, and model performance, recommending improvements across the stack.

Product Innovation & Evangelism

• Act as an internal AI product evangelist — identifying, championing, and prototyping new AI-powered use cases.
• Collaborate with stakeholders to shape AI product concepts and contribute to roadmap development.
• Lead internal PoCs, technical demos, and feasibility assessments.
• Stay current with the evolving AI landscape and evaluate emerging tools, models, and techniques for adoption.