Manager, Forward Deployed Engineer - AI/ML and Cloud Architecture

About the Role

Johnson & Johnson's JJT India Capability Center in Hyderabad is hiring a seasoned Manager, Forward Deployed Engineer specializing in AI, Machine Learning, Generative AI, Agentic AI, and cloud infrastructure. This pivotal role collaborates extensively across business, product, data science, engineering, architecture, security, and compliance teams to convert critical healthcare, clinical, scientific, and enterprise challenges into deployable AI-driven solutions. The position demands expertise at the crossroad of solution architecture, hands-on engineering, and rapid deployment, focusing on cloud-native, responsible AI architectures within AWS and Google Cloud Platform (GCP).

Key Responsibilities

• Collaborate directly with business, product, clinical, scientific, data science, engineering, and technology teams to identify impactful AI/ML use cases and transform them into scalable solutions.
• Quickly prototype, validate, iterate, and implement AI-enhanced products, workflows, and platform features in cooperation with users and delivery teams.
• Design scalable cloud-native architectures involving serverless, containerization, microservices, event-driven systems, data lakes, lakehouses, and hybrid cloud models across AWS and GCP.
• Develop Generative AI frameworks leveraging large language models, Retrieval-Augmented Generation (RAG), semantic search, knowledge graphs, and integrate enterprise knowledge bases.
• Architect Agentic AI systems with autonomous workflows, multi-agent orchestration, tool integrations, operational guardrails, and human-in-the-loop oversight for enterprise scenarios.
• Translate complex and ambiguous business demands into secure, scalable, and production-ready technical solutions in collaboration with cross-functional global teams.
• Assess and advise on cloud-native AI/ML services, data platforms, compute resources, integration methods, and automation frameworks aligned with enterprise standards.
• Implement best practices for prompt engineering, including lifecycle management, versioning, evaluation, and reuse to support robust Generative AI delivery.
• Ensure architectural solutions meet benchmarks for performance, security, privacy, regulatory compliance, cost efficiency, and operational resilience in healthcare-regulated environments.
• Provide hands-on technical leadership and mentorship to engineering teams locally and globally through design reviews, code guidance, and establishing technical standards.
• Champion DevOps and MLOps integration, encompassing CI/CD, infrastructure as code, automated testing, model deployment automation, monitoring, alerting, and release governance.
• Partner with governance, privacy, cybersecurity, quality, and compliance teams to align solutions with enterprise policies and regulatory mandates.
• Execute responsible AI governance, conducting risk assessments, ensuring model explainability and transparency, validation processes, monitoring, and preparation for regulatory scrutiny.
• Drive technology roadmaps, evaluate solutions, lead proof-of-concept and minimum viable product (MVP) phases, manage user feedback loops, and spearhead production rollout and modernization efforts.
• Serve as a trusted technical advisor by bridging strategic vision, architecture design, engineering execution, adoption, and demonstrable business impact.

Required Skills and Experience

• Proficient with AWS and GCP cloud offerings including compute, storage, networking, security, AI/ML services, and observability tools.
• Proven experience delivering enterprise-level AI/ML solutions utilizing modern cloud architecture within multinational technology organizations.
• Deep knowledge of cloud architectures including microservices, container orchestration (Kubernetes), serverless, event-driven design, API integrations, data lakes/lakehouses, and distributed data processing.
• Comprehensive understanding of AI/ML lifecycle processes: data preparation, feature engineering, model training and evaluation, deployment, monitoring, retraining, and governance.
• Hands-on expertise designing Generative AI solutions with large language models, RAG techniques, semantic and vector search, enterprise knowledge retrieval, and knowledge graph data structures.
• Technical familiarity with Agentic AI frameworks involving multi-agent orchestration, autonomous workflows, tools integration, planning mechanisms, guardrails, and enterprise embedding.
• Skilled in prompt engineering practices including prompt lifecycle management, evaluation, reusable pattern development, and operational controls for Generative AI systems.
• Experience with DevOps tools and pipelines: continuous integration/continuous deployment (CI/CD), Git workflows, infrastructure as code, containerization, and environment orchestration.
• Expertise in MLOps processes including automated model deployment, model registries, experiment tracking, monitoring, and drift detection.
• Ability to design cloud solutions that ensure security, compliance, resilience via identity & access management, encryption, network safeguards, logging, and audits.
• Knowledge of AI governance frameworks: responsible AI adoption, risk analysis, model explainability, human oversight, validation, monitoring, and readiness for regulation.
• Experience in embedding engineering models focused on rapid discovery, prototype validation, production implementation, and adoption facilitation.
• Excellent stakeholder engagement and communication skills capable of articulating complex architectural decisions to technical and business audiences.

Preferred Experience

• Background in clinical development, pharmaceutical research, healthcare, life sciences, medical devices, or other regulated sectors relevant to Johnson & Johnson.
• Understanding of clinical workflows, trial data management, regulated data storage, privacy laws, Good Practice (GxP) guidelines, and compliance-focused tech delivery.
• Familiarity with AWS services like SageMaker, Lambda, ECS/EKS, S3, Glue, Redshift, IAM, and CloudWatch.
• Knowledge of GCP products including Vertex AI, BigQuery, Cloud Storage, GKE, Cloud Run, Cloud Functions, Pub/Sub, IAM, and Cloud Monitoring.
• Exposure to data engineering and analytic platforms such as Databricks, Spark, orchestration systems, and modern analytics ecosystems.
• Relevant certifications such as AWS Solutions Architect, AWS Machine Learning Specialty, Google Professional Cloud Architect, or Google Professional Machine Learning Engineer.

Educational Qualifications

Candidates with a Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related disciplines are preferred.
Acceptable degrees include B.C.A in Computer Applications, B.Sc in Computer Science and Technology or Information Technology, B.Tech/B.E. in Computer Science and Engineering or Information Technology, and B.A in Computer Science.