AI Engineer

We are looking for a hands-on AI Engineer to drive the design, development, and innovation of AI capabilities within our enterprise-grade AI platform — a secure, internal environment offering services such as document translation, intelligent chatbots, LLM APIs, and other AI-powered workflows.

You will own the end-to-end lifecycle of Generative AI, Agentic AI, and applied AI/ML solutions — from ideation and rapid prototyping, through model training and fine-tuning where needed, to inference and production deployment in collaboration with engineering teams. While our preferred deployment environment is Azure, the role is not strictly cloud-native; we value engineers who can deliver robust AI solutions across diverse stacks. This role blends deep technical expertise with product thinking to deliver tangible business value through AI.

Key Responsibilities

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.