Manager - Forward Deployed Engineer

Company: Johnson & Johnson - JJT India Capability Center

Location: Hyderabad, India

Function: Technology - AI/ML, Generative AI, Cloud Engineering & Digital Solutions

Experience: 10+ Years

Employment Type: Full time

Level: Manager

About Johnson & Johnson

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions.

Role Overview

Johnson & Johnson JJT India Capability Center is seeking an experienced Manager - Forward Deployed Engineer with strong expertise in AI/ML, Generative AI, Agentic AI, cloud architecture, and enterprise technology delivery.

The role will operate at the intersection of business problem-solving, hands-on engineering, solution architecture, and rapid delivery. The incumbent will work closely with business, product, data science, engineering, architecture, security, privacy, quality, compliance, and other stakeholders to translate high-value healthcare, clinical, scientific, and enterprise technology needs into secure, scalable, compliant, and production-grade AI solutions.

The role will define and implement cloud-native AI/ML, Generative AI, Agentic AI, and responsible AI architecture patterns across AWS and Google Cloud Platform (GCP) while providing hands-on technical leadership to global engineering teams.

Experience in clinical development, life sciences, healthcare, pharmaceutical R&D;, or regulated data environments will be highly preferred.

Key Responsibilities 1. AI/ML & Generative AI Engineering

- Partner with business, product, clinical, scientific, data science, engineering, and technology stakeholders to identify high-impact AI/ML use cases.
- Translate ambiguous business requirements into deployable AI/ML and Generative AI solutions.
- Rapidly prototype, validate, iterate, and deploy AI-enabled products, workflows, and platform capabilities.
- Design and implement Generative AI solutions using:
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- Vector and semantic search
- Enterprise knowledge retrieval
- Knowledge graphs
- Enterprise knowledge integration

- Establish prompt engineering, prompt management, evaluation, versioning, reuse, and lifecycle management practices.

1. Agentic AI Architecture

- Architect and implement Agentic AI solutions for enterprise use cases.
- Design autonomous AI workflows and multi-agent orchestration patterns.
- Evaluate and implement agentic frameworks, tool integration, planning patterns, and enterprise integrations.
- Establish appropriate guardrails and human-in-the-loop controls.
- Ensure agentic solutions are secure, reliable, explainable, and operationally scalable.

1. Cloud Architecture

- Design scalable and reusable cloud architecture patterns across AWS and GCP.

- Architect solutions using
- Microservices

- Containers and Kubernetes
- Serverless architecture
- Event-driven architecture
- API-based integration
- Data lakes and lakehouses
- Hybrid cloud
- Distributed processing

- Evaluate and recommend appropriate cloud-native AI/ML services, data platforms, compute options, integration patterns, and automation frameworks.
- Design solutions considering performance, scalability, reliability, security, privacy, cost optimization, and operational resilience.

1. Hands-on Technical Leadership

- Provide hands-on technical leadership to engineering teams in Hyderabad and global delivery teams.

- Support implementation through

- Reference architectures
- Design reviews
- Code-level guidance
- Technical standards
- Architecture decisions
- Proof-of-concepts
- MVP development

- Guide engineering teams in converting prototypes into production-ready solutions.
- Influence technical direction and establish reusable enterprise architecture patterns.

1. DevOps & MLOps

- Drive adoption of DevOps and MLOps practices across engineering teams.





- Implement and improve
- CI/CD pipelines
- Git-based workflows

- Infrastructure as Code
- Automated testing
- Model deployment automation
- Model registry
- Experiment tracking
- Model monitoring
- Drift detection
- Automated release governance

- Establish monitoring, alerting, reliability, and operational practices for AI/ML solutions.

1. Security, Compliance & Responsible AI

- Collaborate with governance, privacy, cybersecurity, quality, and compliance teams.

- Ensure AI/ML solutions align with enterprise security and regulatory standards.
- Implement responsible AI and AI governance practices, including:
- AI risk assessments
- Model explainability

- Transparency
- Model validation
- Monitoring
- Human-in-the-loop controls
- Regulatory readiness

- Ensure appropriate identity and access management, encryption, network controls, logging, auditability, and data privacy.

1. Forward-Deployed Engineering & Stakeholder Management

- Operate as a trusted technical partner embedded with business and product teams.
- Lead rapid discovery, solution shaping, prototyping, user validation, production delivery, and adoption support.
- Bridge strategy, architecture, engineering execution, adoption, and measurable business outcomes.
- Communicate complex technical and architecture decisions effectively to both technical and business audiences.
- Collaborate with global product, engineering, data science, architecture, security, infrastructure, quality, and business stakeholders.

1. Technology Roadmap & Innovation

- Support technology roadmaps, evaluations, proof-of-concepts, MVP delivery, production rollout, and modernization initiatives.
- Evaluate emerging AI/ML, Generative AI, Agentic AI, cloud, data, and automation technologies.
- Drive continuous improvement in engineering productivity, solution quality, scalability, reliability, and cost efficiency.

Required Skills & Experience
- 10+ years of overall technology experience with significant hands-on experience in:
- Software/Cloud Engineering
- Solution Architecture
- AI/ML
- Generative AI
- Data Engineering
- Enterprise Technology Delivery

- Strong hands-on experience with AWS and GCP.
- Strong experience delivering enterprise-grade AI/ML solutions within large/global technology organizations.
- Strong understanding of cloud-native architecture patterns, including:

- Microservices
- Kubernetes
- Containers
- Serverless
- Event-driven architecture
- API integration
- Data lake/lakehouse
- Distributed processing

- Strong understanding of the AI/ML lifecycle:

- Data preparation
- Feature engineering
- Model training
- Model evaluation
- Deployment
- Monitoring
- Retraining
- Governance

- Strong experience designing Generative AI solutions using LLMs, RAG, vector search, semantic search, enterprise knowledge retrieval, and/or knowledge graphs.
- Hands-on understanding of Agentic AI architecture, including:

- Agent frameworks
- Multi-agent orchestration
- Autonomous workflows
- Tool use
- Planning patterns
- Guardrails
- Enterprise integration

- Strong understanding of prompt engineering and prompt lifecycle management.
- Strong knowledge of DevOps practices, CI/CD, Git workflows, automated deployment, Infrastructure as Code, containerization, and environment management.
- Experience with MLOps concepts and tooling.
- Strong understanding of cloud security, IAM, encryption, network controls, logging, monitoring, auditability, and compliance.
- Knowledge of responsible AI, AI governance, risk assessment, explainability, transparency, validation, and monitoring.
- Demonstrated experience working in a forward-deployed or embedded engineering model.
- Strong stakeholder management, communication, problem-solving,



and technical leadership skills.

Preferred Skills Healthcare / Life Sciences
- Experience in:
- Clinical development
- Pharmaceutical R&D;
- Healthcare
- Life Sciences
- Medical Technology
- Regulated technology environments

- Understanding of clinical development workflows and clinical trial data.
- Knowledge of regulated data platforms, privacy requirements, GxP considerations, and compliance-driven technology delivery.
- Prior experience working in an Innovative Pharma company in an R&D; environment is an added advantage.

AWS Experience with relevant AWS services such as:
- Amazon SageMaker
- AWS Lambda
- Amazon ECS/EKS
- Amazon S3
- AWS Glue
- Amazon Redshift
- AWS IAM
- Amazon CloudWatch
- Other AWS data, AI/ML, security, and observability services

Google Cloud Platform Experience with relevant GCP services such as:
- Vertex AI
- BigQuery
- Cloud Storage
- Google Kubernetes Engine (GKE)
- Cloud Run
- Cloud Functions
- Pub/Sub
- IAM
- Cloud Monitoring

Data & Analytics
- Databricks
- Apache Spark
- Data lake/lakehouse architecture
- Data engineering platforms
- Data orchestration tools
- Modern analytics platforms

Certifications

Preferred certifications include

- AWS Solutions Architect
- AWS Machine Learning Specialty
- Google Professional Cloud Architect
- Google Professional Machine Learning Engineer
- Relevant AI/ML, cloud, or architecture certifications

Education
- Bachelor s or Master s degree in:
- Computer Science
- Engineering
- Data Science
- Information Technology
- or a related discipline

Experience Requirements
- 10+ years of overall technology experience.
- 10+ years of experience designing and implementing cloud-native solutions using AWS, GCP, or multi-cloud environments.
- Strong hands-on experience in solution delivery, architecture discussions, design reviews, technical roadmaps, MVP development, and cross-functional delivery.
- Prior experience leading engineering/technical teams and delivering enterprise technology solutions.

Key Competencies
- Forward-deployed engineering mindset
- Strong ownership and rapid problem-solving
- Hands-on engineering and delivery orientation
- Ability to operate effectively in ambiguous business environments
- Ability to convert user needs into scalable technical solutions
- Cloud-native solution design across AWS and GCP
- AI/ML platform architecture and MLOps
- Generative AI, LLM, RAG, semantic search, and knowledge graph expertise
- Agentic AI and autonomous workflow architecture
- DevOps and automation mindset
- Security, privacy, compliance, and governance orientation
- Responsible AI and AI governance
- Strong global stakeholder management
- Technical leadership and influencing skills
- Ability to establish reusable enterprise patterns and drive adoption

Success Measures Success in this role will be measured by:
- 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.
- Delivery of enterprise-grade Generative AI and Agentic AI architectures using LLMs, RAG, semantic search, knowledge graphs, and autonomous workflow patterns.
- Improved speed, reliability, scalability, and cost efficiency through cloud-native architecture.
- Successful adoption of DevOps and MLOps practices across delivery teams.
- Strong alignment of AI/ML solutions with enterprise architecture, security, privacy, and compliance standards.
- Effective implementation of responsible AI controls, AI risk assessments, explainability, human-in-the-loop mechanisms, validation, and monitoring.
- Improved adoption, usability, and measurable business value across global business, clinical, data science, engineering, platform, and Hyderabad capability center teams.

Disclaimer: This has been sourced from a public domain and may have been modified by Naukri.com to improve clarity for our users. We encourage job seekers to verify all details directly with the employer via their official channels before applying.

📌 Mgr, Forward Deployed Engineer (Hyderabad)
🏢 Agile Technology
📍 Hyderabad