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.
Location: Hyderabad, India | Organization: Johnson & Johnson, JJT India Capability Center | Function: Technology, AI/ML, Generative AI, Cloud Engineering and Digital Solutions
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 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 & Johnson’s 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
Bachelor’s or Master’s 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.
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.
Johnson & Johnson is an Affirmative Action and Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, veteran status, or any other protected characteristic.
Required Skills
Preferred Skills
Analytical Reasoning, Consulting, Cost Management, Developing Others, Human-Computer Interaction (HCI), Inclusive Leadership, Leadership, People Performance Management, Performance Measurement, Product Development, Product Strategies, Project Management Methodology (PMM), Research and Development, Resource Management, Software Development Management, Strategic Supply Chain Management, Team Management
