We are the people who give possibilities purpose BD is one of the largest global medical technology companies in the world. Advancing the world of health™ is our Purpose, and it’s no small feat. It takes the imagination and passion of all of us—from design and engineering to the manufacturing and marketing of our billions of MedTech products per year—to look at the impossible and find transformative solutions that turn dreams into possibilities.


 Job Description Role Summary 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.
Required Skills & Experience 2–4 years in applied AI/ML engineering, with demonstrated delivery of production Generative and Agentic AI solutions. Ability to build AI solutions across Generative AI and Agentic AI, including training and fine-tuning language models when required and deploying them for inference . Proven experience building Agentic AI systems — multi-agent orchestration, reusable agent skills/tools, and agent interoperability (A2A, ACP, MCP).
Hands-on experience designing and shipping advanced RAG pipelines in production — including hybrid retrieval, re-ranking, query transformation, and systematic evaluation — over large, unstructured document collections. Hands-on