Forward Deployed Engineer

Please find below the JD:

Key Responsibilities

• Define scalable, secure, resilient enterprise application architectures.
• Design microservices, APIs, event-driven systems, distributed applications, and cloud-native platforms.
• Assess applications and recommend modernization, re-platforming, and cloud-transformation strategies.
• Create architecture blueprints, solution designs, technical roadmaps, and decision records.
• Develop and troubleshoot full-stack solutions using Java and/or Python with React and/or Angular.
• Build Spring Boot services, REST APIs, integrations, and production-grade proof-of-concepts.
• Architect AWS solutions using services such as EKS, Lambda, S3, RDS, DynamoDB, API Gateway, EventBridge, SQS, SNS, CloudWatch, and Bedrock.
• Build LLM applications, RAG solutions, AI Agents, Agentic workflows, and tool-enabled automation.
• Apply AI-native engineering practices across requirements, development, testing, code review, documentation, and CI/CD.
• Lead client workshops and clearly communicate architecture decisions, risks, and trade-offs.
• Translate Life Sciences and Healthcare requirements into secure, compliant technology solutions.
• Mentor engineering teams and promote architecture and development best practices.

Client and Domain Leadership

• Work directly with client stakeholders to understand strategic priorities, technology challenges, and business requirements.
• Facilitate architecture workshops, technical discovery sessions, design reviews, and solution demonstrations.
• Translate Life Sciences and Healthcare requirements into practical enterprise technology solutions.
• Advise clients on application modernization, cloud transformation, platform engineering, and AI adoption.
• Communicate complex technical concepts clearly to business leaders, architects, engineering teams, and delivery stakeholders.
• Support solution shaping, technical estimations, delivery planning, risk management, and executive-level technical presentations.
• Provide technical leadership across Life Sciences and Healthcare areas such as Clinical and R&D, Commercial and Sales, Supply Chain, Manufacturing, Patient and Healthcare Platforms, Regulatory and Compliance, Data and Analytics, and Quality.

Mandatory Skills Required

• Architecture and Technical Leadership
• 15–20 years of relevant technology experience.
• Strong enterprise application and solution architecture experience.
• Proven ability to architect complex, large-scale, secure, resilient, and highly available platforms.
• Deep understanding of microservices, REST APIs, event-driven architecture, distributed systems, and cloud-native application patterns.
• Experience creating architecture blueprints, technical roadmaps, solution designs, and Architecture Decision Records.
• Strong application modernization, re-platforming, and re-architecture experience.
• Ability to operate effectively in a client-facing architecture and engineering role.

Full-Stack Engineering

• Strong hands-on development experience in Java and/or Python.
• Hands-on experience with Spring Boot, REST APIs, microservices, and distributed applications.
• Strong understanding of API design, authentication, authorization, databases, and integration patterns.
• Hands-on frontend development experience with React and/or Angular.
• Ability to work across frontend and backend layers and troubleshoot end-to-end application issues.
• Demonstrated ability to contribute directly to code, prototypes, proofs of concept, and production solutions.

AWS Cloud Architecture

• Strong hands-on experience with AWS architecture and cloud-native application development.
• Practical experience with relevant AWS services, including: EC2, EKS, Lambda, S3, RDS, DynamoDB, VPC, IAM, CloudWatch, API Gateway, EventBridge, SQS and SNS
• Amazon Bedrock and related AI/ML services
• Experience with cloud migration, application modernization, and cloud optimization.
• Strong understanding of cloud networking, security, scalability, resiliency, high availability, and observability.

Generative AI and Agentic Engineering

• Strong practical experience implementing Generative AI and Agentic AI solutions.
• Hands-on experience designing and developing AI agents and agentic workflows for enterprise use cases.
• Experience with LLM-based applications, RAG, tool and function calling, orchestration, and AI-enabled automation.
• Experience with MCP, LangChain, LangGraph, Amazon Bedrock, or similar technologies.
• Experience delivering AI-enabled applications from proof of concept through production deployment.
• Understanding of AI governance, security, observability, evaluation, and responsible AI practices.
• Ability to identify and implement AI-driven automation opportunities across engineering and business processes.

Harness Engineering and AI-Native Delivery

• Hands-on experience with Harness Engineering or comparable AI-native software engineering approaches.
• Experience using AI engineering agents across requirements, architecture, development, testing, code review, documentation, and CI/CD.
• Ability to establish AI-assisted and agentic SDLC practices across engineering teams.

Life Sciences and Healthcare

• Strong Life Sciences and/or Healthcare domain experience.
• Experience working with pharmaceutical, biotechnology, medical device, healthcare, or related organizations.
• Ability to translate domain and business requirements into enterprise technology solutions.
• Experience in one or more of the following areas: Clinical and R&D, Commercial and Sales, Supply Chain, Pharma or Clinical Manufacturing, Patient and Healthcare Platforms, Regulatory and Compliance, Data and Analytics or Quality

Nice to Have Skills

• Exposure to Azure and/or Google Cloud Platform.
• Experience with Claude Code, Cursor, GitHub Copilot, Devin, or equivalent AI coding and engineering tools.
• Broader experience with AI/ML platforms and agent-orchestration technologies beyond Amazon Bedrock, LangChain, and LangGraph.
• Experience introducing AI-assisted engineering practices across multiple teams or enterprise delivery programs.
• Experience defining reusable accelerators, reference architectures, engineering guardrails, or governance models for AI-native software delivery

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