The APAC Business Technology Team within Johnson & Johnson Innovative Medicine, is looking for an extraordinary GenAI and Machine Learning Engineering Manager who is passionate about crafting, developing, and fielding data science solutions that drive impact for HCP (Health Care Professional) and patients. Specifically, we are looking for
A senior, hands-on leader, to build and scale production-grade full-stack GenAI solutions with a strong emphasis on backend systems, data, and AI/ML engineering. This role is expected to operate as a true partner to Business leaders- co-framing problems, influencing solution strategy, and co-owning measurable user and business outcomes - not as an execution-only engineering function.
Responsibilities Product & Business Partnership
- Co-own problem discovery with Product/Business: translate objectives into well-defined, testable problem statements, user journeys, and technical hypotheses.
- Challenge assumptions and requirements using user insights, data realities, feasibility constraints, and system impact recommend alternatives when they better achieve outcomes.
- Define and track success metrics and drive iteration when targets aren't met.
- Communicate clearly with executive, business, and engineering audiences - articulating trade-offs, decisions, and rationale.
Solution Leadership
- Lead end-to-end solution for GenAI products: APIs, services, data pipelines, orchestration, LLM integration, retrieval, tool-calling, and UI/UX touchpoints as needed.
- Make disciplined architectural trade-offs across performance, reliability, cost, extensibility, maintainability, and time-to-value.
- Design for enterprise realities: identity/access, data residency, compliance constraints, multi-environment deployments, and integration with core systems.
- Drive platform thinking: prioritize reusable components (libraries, templates, shared services) over one-off builds.
Agentic & Full-Stack GenAI Applications
- Build and scale agentic GenAI applications that solve multi-step workflows on cloud platforms such as AWS and Azure.
- Define and implement robust LLM patterns (prompting, RAG, tool orchestration, validation, and evaluation).
- Implement LLMOps and lifecycle governance for models and prompts, including versioning, evaluation and monitoring.
- Ensure solutions meet enterprise standards for quality, reliability, latency, and cost.
- Embed Responsible AI, security, and compliance by design, partnering with Risk, Legal, and Security.
Engineering Excellence & DevOps
- Provide hands-on leadership across the SDLC: design reviews, coding standards, testing strategy, code reviews, security reviews, and operational readiness.
- Establish robust CI/CD and standardized environments for services and AI components utilizing cloud services.
- Ensure operational excellence through observability, incident management, reliability practices, and cost optimization.
Research and Innovation:
- Stay current with the latest advancements in Generative AI, cloud technologies, and ML/LLM Ops practices, and proactively translate relevant insights into team and platform adoption.
- Evaluate emerging tools, techniques, and methodologies to enhance GenAI capabilities, developer productivity, and operational efficiency, integrating them into platforms and solutions where they add value.
- Lead targeted experimentation and proofs of concept, scaling successful ideas into robust, production-ready solution
Required Knowledge, Skills and Abilities:
- Strong leadership skill with ability to influence the thought process and drive alignment.
- Proven track record in designing and building GenAI solutions, with a strong expertise in prompt engineering, LlamaIndex, Langchain, RAG, Agentic AI workflow, Knowledge graph, AI red teaming libraries, LLM monitoring and evaluation.
- Over 7+ years of experience in the software development lifecycle as a developer, with a focus on writing production code.
- Must have hands-on experience working with software development toolkits, and devOps automation like Kubernetes, Airflow, Jenkins, Jira, Confluence and Git.
- Excellent programming skills in Python and proficiency in PySpark.
- Experience in DataBricks, AWS bedrock, Microsoft Copilot Studio and Azure ML is plus.
- Prior experience in Pharma commercial is a plus.
- Excellent communication skills and a demonstrated ability to collaborate effectively with cross-functional teams.
- Self-motivated and highly driven individual who can thrive in ambiguous requirements.
