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 experience building and orchestrating agent skills — authoring and managing reusable skill definitions (e.g., skills.md/Claude Skills-style capability files) that can be dynamically discovered and invoked by LLMs.
- Experience building custom MCP (Model Context Protocol) servers — exposing tools, resources, and data sources to LLMs/agents through standardized, interoperable interfaces.
- Proven experience building entity extraction and document understanding solutions at scale across diverse, unstructured document formats.
- Practical experience with LLM orchestration frameworks and vector/retrieval systems (e.g., LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, Semantic Kernel; FAISS, Qdrant, Pinecone, Weaviate, Azure AI Search — or equivalents).
- Strong proficiency in Python, including API development (FastAPI/Flask), async/concurrent programming, data wrangling, and rapid prototyping (Streamlit, Gradio, etc.).
- Hands-on experience integrating at least one major LLM provider (e.g., OpenAI, Azure OpenAI, Anthropic/Claude, Hugging Face, Cohere, Meta Llama).
- Solid grasp of prompt engineering, context engineering, and latency-vs-cost trade-offs.
- Working familiarity with the Azure AI ecosystem (Azure AI Studio/Foundry, Azure OpenAI, Azure AI Services such as Vision, Translator, and Document Intelligence) — or willingness to ramp up quickly.
- Experience with the Anthropic/Claude ecosystem (Claude Skills, Claude Code, Agent SDK).
- Familiarity with fine-tuning and model optimization techniques (PEFT, LoRA, quantization, distillation, pruning).
Preferred Qualifications( Good to have)
- Experience with experiment tracking and evaluation tools (e.g., LangSmith, MLflow)
- Familiarity with deep learning / classical ML libraries (PyTorch, TensorFlow, Scikit-learn) and CV/NLP toolkits (Transformers, spaCy, OpenCV), and exposure to computer vision (image classification, object detection, segmentation) where appropriate — a plus, not mandatory.
- A strong track record in Kaggle competitions, AI/ML hackathons, or similar applied challenges is a definite plus.
Collaboration & Integration
- Partner with Full Stack Developers to translate AI capabilities into usable, well-documented APIs.
- Coordinate with DevOps and Cloud Engineers to align AI features with infrastructure, security, and deployment requirements.
- Participate in design reviews and planning sessions to ensure smooth handoff of AI features into production.
What You'll Bring
- A builder's mindset — you turn ideas into working prototypes quickly.
- Full-spectrum capability — you pick the right approach (GenAI, agents, deep learning, or classical ML) for the problem at hand, and can train or fine-tune models when off-the-shelf won't do.
- Product intuition — you think beyond code and understand business impact.
- Collaborative spirit — you communicate clearly across engineering, product, and infrastructure teams.
- Curiosity — you stay ahead of the rapidly evolving GenAI and Agentic AI landscape.
Why Join Us?
To find purpose in the possibilities, we need people who can see the bigger picture, who understand the human story that underpins everything we do. We welcome people with the imagination and drive to help us reinvent the future of healthcare. At BD, you’ll discover a culture in which you can learn, grow and thrive.
We believe that when people connect in person, we learn faster, collaborate more deeply, and build a stronger culture. Join us and enjoy a culture where face-to-face collaboration supports your learning, your progress, and your success.
To learn more about BD visit https://bd.com/careers.
Becton, Dickinson, and Company is an Equal Opportunity Employer. We evaluate applicants without regard to race, color, religion, age, sex, creed, national origin, ancestry, citizenship status, marital or domestic or civil union status, familial status, affectional or sexual orientation, gender identity or expression, genetics, disability, military eligibility or veteran status, and other legally protected characteristics.
Required Skills
Optional Skills
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Primary Work Location
IND Bengaluru - Technology Campus
Additional Locations
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