Manager, Forward Deployed Engineer

About the role

Johnson & Johnson’s JJT India Capability Center is seeking a Manager, Forward Deployed Engineer to lead AI/ML, Generative AI, and cloud architecture initiatives. The role bridges business problem‑solving, hands‑on engineering, and rapid delivery of production‑grade AI solutions for healthcare and life‑science applications.

Key responsibilities

• Partner with product, clinical, data‑science and engineering teams to identify high‑impact AI use cases and translate them into deployable solutions.
• Design and implement scalable, cloud‑native architecture patterns across AWS and GCP, including serverless, containerized micro‑services, event‑driven, data‑lake and hybrid models.
• Develop Generative AI solutions using large language models, Retrieval‑Augmented Generation, semantic search, knowledge graphs and enterprise knowledge integration.
• Architect Agentic AI workflows, multi‑agent orchestration, tool integration, guardrails and human‑in‑the‑loop controls.
• Drive DevOps and MLOps practices: CI/CD pipelines, infrastructure‑as‑code, automated testing, model deployment, monitoring and governance.
• Ensure solutions meet performance, security, privacy, compliance and cost‑optimization requirements in a regulated healthcare environment.

Required profile

• Strong stakeholder management and communication skills to explain complex architecture decisions to both technical and business audiences.
• Proven ability to work in forward‑deployed or embedded engineering models, rapidly prototyping, validating and delivering production solutions.

Required skills

• AWS and Google Cloud Platform services (compute, storage, networking, security, AI/ML services).
• Cloud architecture patterns: micro‑services, containers, Kubernetes, serverless, event‑driven, API integration, data lake/lakehouse.
• AI/ML lifecycle expertise: data preparation, model training, evaluation, deployment, monitoring, retraining, governance.
• Generative AI development with LLMs, RAG, vector/semantic search, knowledge graphs.
• Agentic AI frameworks, multi‑agent orchestration, prompt engineering and lifecycle management.
• DevOps tools: CI/CD, Git, IaC, containerization, environment management.
• MLOps tooling: model registry, experiment tracking, drift detection, automated deployment.
• Security and compliance: IAM, encryption, network controls, logging, auditability.
• AI governance: responsible AI, risk assessment, explainability, human‑in‑the‑loop controls.