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 workplace 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 explainability and transparency, human-in-the-loop controls, validation, monitoring, and regulatory readiness.
- Demonstrated ability to work in forward-deployed or embedded engineering models, including rapid discovery, solution shaping, prototyping, user validation, production delivery, and adoption support.
- Strong stakeholder management and communication skills, with the ability to explain complex architecture decisions to both technical and business audiences.

Preferred Skills

- Experience working in clinical development, pharmaceutical R&D;, healthcare, life sciences, medical technology, or other regulated domains relevant to Johnson & Johnsons business environment.
- Understanding of clinical development workflows, clinical trial data, regulated data platforms, privacy requirements, GxP considerations, and compliance-driven technology delivery.
- Experience with AWS services such as SageMaker, Lambda, ECS/EKS, S3, Glue, Redshift, IAM, CloudWatch, and related data or AI/ML services.




- Experience with GCP services such as Vertex AI, BigQuery, Cloud Storage, GKE, Cloud Run, Cloud Functions, Pub/Sub, IAM, and Cloud Monitoring.
- Exposure to data engineering platforms, lakehouse architectures, Databricks, Spark, orchestration tools, and modern analytics platforms.
- Cloud or architecture certifications such as AWS Solutions Architect, AWS Machine Learning Specialty, Google Professional Cloud Architect, or Google Professional Machine Learning Engineer.

Education and Experience

- Bachelors or Masters degree in Computer Science, Engineering, Data Science, Information Technology, or a related discipline.
- 10+ years of overall technology experience, including significant experience in hands-on engineering, solution architecture, cloud platforms, data engineering, AI/ML, Generative AI, or enterprise solution delivery.
- 10+ years of experience designing and implementing cloud-native solutions on AWS, GCP, or multi-cloud environments.
- Prior experience leading hands-on solution delivery, architecture discussions, design reviews, technical roadmaps, MVP development, and cross-functional delivery teams.
- Prior experience of working in Innovative Pharma company in R&D; domain would be added advantage.
- Senior engineering leader specializing in AI/ML, Generative AI and Agentic AI solutions.
- Strong experience designing cloud-native solutions across AWS and GCP.
- Expertise in LLMs, RAG architectures, AI governance and MLOps practices.
- Ability to convert complex business problems into scalable AI-enabled products.
- Healthcare, Clinical or Pharmaceutical domain exposure highly preferred.

Key Competencies

- Forward-deployed engineering mindset with strong ownership, rapid problem-solving, and hands-on delivery orientation
- Ability to operate effectively in ambiguous business environments and convert user needs into scalable technical solutions
- Cloud-native solution design across AWS and GCP
- AI/ML platform architecture and MLOps enablement
- Generative AI, RAG, semantic search, knowledge graph, and Agentic AI architecture capability
- DevOps' mindset with focus on automation, reliability, and continuous delivery
- Security, compliance, and governance orientation
- Responsible AI, AI governance, explainability, validation, monitoring, and regulatory readiness orientation
- Strong collaboration with global product, engineering, data science, architecture, security, quality, and business stakeholders
- Ability to influence technical direction, establish reusable enterprise patterns, and drive adoption through hands-on execution

Success Measures

- Delivery of scalable, secure, reusable, and production-ready AI/ML solutions and reference architectures.
- Successful conversion of ambiguous business problems into validated prototypes, MVPs, and production deployments with measurable stakeholder impact.
- Delivery of enterprise-ready Generative AI and Agentic AI architectures using LLMs, RAG, semantic search, knowledge graphs, and autonomous workflow patterns.
- Successful implementation of cloud patterns that improve speed, reliability, and cost efficiency.
- Effective adoption of DevOps and MLOps practices across delivery teams.
- Strong alignment of AI/ML solutions with enterprise architecture, security, and compliance expectations.
- Effective implementation of responsible AI controls, AI risk assessments, model explainability, human-in-the-loop mechanisms, validation, monitoring, and regulatory readiness practices.
- Improved adoption, usability, and measurable value realization across global business, clinical, data science, engineering, platform, and Hyderabad capability center teams.

📌 Manager, Forward Deployed Engineer (Hyderabad)
🏢 Johnson u0026 Johnson
📍 Hyderabad