Manager, Forward Deployed Engineer

Job description

Johnson & Johnson JJT India Capability Center, Hyderabad is seeking an experienced Mgr, Forward Deployed Engineer focused on AI/ML, Generative AI, Agentic AI, and cloud architecture. This role will work closely with business, product, data science, engineering, architecture, security, and compliance stakeholders to translate high-value healthcare, clinical, scientific, and enterprise technology needs into production-grade AI solutions. The Mgr, Forward Deployed Engineer will operate at the intersection of business problem-solving, hands-on engineering, solution architecture, and rapid delivery. The role will define and implement cloud-native, AI/ML, Generative AI, Agentic AI, and responsible AI architecture patterns; build and deploy prototypes and production solutions; and guide engineering teams in delivering reliable, compliant, and high-performing solutions across AWS and Google Cloud Platform (GCP). Experience in clinical development, life sciences, healthcare, pharmaceutical R&D, or regulated data environments will be highly preferred. **Key Responsibilities** - Partner directly with business, product, clinical, scientific, data science, engineering, and technology stakeholders to identify high-impact use cases and translate them into deployable AI/ML and Generative AI solutions. - Rapidly prototype, validate, iterate, and deploy AI-enabled products, workflows, and platform capabilities in close partnership with users and delivery teams. - Design scalable and reusable cloud architecture patterns across AWS and GCP, including serverless, containerized, microservices-based, event-driven, data lake, lakehouse, and hybrid cloud patterns. - Design and implement Generative AI solutions leveraging large language models, Retrieval-Augmented Generation architecture patterns, semantic search, knowledge graphs, and enterprise knowledge integration. - Architect Agentic AI solutions, including autonomous AI workflows, multi-agent orchestration, agentic frameworks, tool integration, guardrails, and human-in-the-loop controls for enterprise use cases. - Embed with global product, data science, engineering, security, infrastructure, architecture, and business teams to convert ambiguous business requirements into secure, scalable, and production-ready technical solutions. - Evaluate and recommend appropriate cloud-native AI/ML services, data platforms, compute options, integration patterns, and automation frameworks aligned to enterprise architecture standards. - Establish prompt engineering, prompt management, evaluation, versioning, reuse, and lifecycle practices for scalable Generative AI delivery. - Ensure architecture decisions meet requirements for performance, reliability, scalability, security, privacy, compliance, cost optimization, and operational resilience in a regulated healthcare environment. - Provide hands-on technical leadership to engineering teams at the Hyderabad capability center and across global delivery teams through implementation support, reference architectures, design reviews, code-level guidance, and technical standards. - Drive adoption of DevOps and MLOps practices, including CI/CD, infrastructure as code, automated testing, model deployment automation, monitoring, alerting, and release governance. - Collaborate with governance, privacy, cybersecurity, quality, and compliance stakeholders to ensure AI/ML solutions align with Johnson & Johnson enterprise standards and regulatory expectations. - Implement responsible AI and AI governance practices, including AI risk assessments, model explainability, transparency, validation, monitoring, and regulatory readiness for AI systems. - Support solution roadmaps, technology evaluations, proof-of-concepts, MVP delivery, user feedback cycles, production rollout, and modernization initiatives for AI/ML, data platforms, and digital solutions. - Act as a trusted technical partner for stakeholders by bridging strategy, architecture, engineering execution, adoption, and measurable business outcomes. **Required Skills and Experience** - Hands-on experience with AWS and GCP cloud services, including compute, storage, networking, security, data platforms, AI/ML services, and observability capabilities. - Strong experience delivering enterprise-grade AI/ML solutions using modern cloud architecture patterns within large, global technology organizations. - Deep understanding of cloud architecture patterns such as microservices, containers, Kubernetes, serverless, event-driven architecture, API-based integration, data lake/lakehouse, and distributed processing. - Strong understanding of AI/ML lifecycle concepts, including data preparation, feature engineering, model training, model evaluation, deployment, monitoring, retraining, and governance. - Experience designing Generative AI solutions using LLMs, including RAG architecture patterns, vector search, semantic search, enterprise knowledge retrieval, and knowledge graph-based architectures. - Hands-on knowledge of Agentic AI architecture, including agent frameworks, multi-agent orchestration, autonomous workflow design, tool use, planning patterns, guardrails, and enterprise integration. - Strong understanding of prompt engineering, prompt lifecycle management, prompt evaluation, reusable prompt patterns, and operational controls for Generative AI applications. - Knowledge of DevOps practices and tools, including CI/CD pipelines, Git-based workflows, automated deployments, infrastructure as code, containerization, and environment management. - Experience with MLOps concepts and tooling for automated model deployment, model registry, experiment tracking, model monitoring, and drift detection. - Ability to design secure, compliant, and resilient cloud solutions with appropriate identity and access management, encryption, network controls, logging, and auditability. - Knowledge of AI governance practices, including responsible AI implementation, AI risk assessment, model explaina