Agentic AI Architect

Ampcus Inc.•San Francisco, CA

1d•Remote

About The Position

The Agentic AI PDLC Engineering Transformation Architect will lead the transformation of CRG’s software engineering organization from traditional Agile development into an AI-augmented, agentic engineering operating model. This leader will define the architecture, engineering practices, governance, platforms, and adoption strategy required to embed AI agents throughout the Product Development Life Cycle (PDLC), enabling engineers, product managers, QA, DevOps, security, validation, and operations teams to work alongside autonomous AI agents. The role combines enterprise architecture, AI engineering, software modernization, platform engineering, DevSecOps, organizational transformation, and clinical software delivery. This individual will build the blueprint that enables CRG engineering teams to deliver software faster, with higher quality, while maintaining regulatory compliance (GxP, FDA 21 CFR Part 11, HIPAA, GDPR, EU CRA).

Requirements

Bachelor’s degree in Computer Science, Engineering, or related discipline.
12 years of enterprise software engineering experience.
8 years leading enterprise architecture initiatives.
5 years leading cloud-native engineering transformations.
Experience leading large Agile organizations (200 engineers preferred).
Experience with enterprise software modernization.
Experience implementing DevSecOps at scale.
Experience delivering regulated healthcare or life sciences software.
Strong understanding of AI engineering platforms.
Experience implementing Generative AI solutions in enterprise environments.
Technical Expertise AI, Generative AI, Large Language Models, Agentic AI, Autonomous Agents, RAG, Knowledge Graphs, Prompt Engineering, Fine Tuning, LLMOps, Model Evaluation, AI Safety, MCP, Context Engineering.
Engineering Python, Java, C#, TypeScript, React, Node.js, REST APIs, GraphQL, Microservices, Kubernetes, Docker, GitHub, Azure DevOps, Terraform, CI/CD.
Cloud Azure, AWS, Google Cloud, Databricks, Snowflake, PostgreSQL, Redis, Vector Databases, Neo4j.
AI Frameworks LangGraph, CrewAI, AutoGen, Semantic Kernel, PydanticAI, LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, Ollama, Azure AI Foundry, GitHub Copilot Enterprise, Cursor.
Leadership Competencies Strategic thinking, Executive communication, Enterprise architecture, Engineering leadership, Innovation, Cross-functional collaboration, Influencing without authority, Technology evangelism, Organizational transformation, Change leadership, Coaching, Executive presentations, Vendor management, Financial acumen.

Nice To Haves

Master’s degree preferred.
Clinical Research, Clinical Trials, Electronic Data Capture, Clinical Data Management, Pharmacovigilance, Medical Devices, Laboratory Systems, Digital Health, Bioinformatics, Healthcare SaaS, Life Sciences, GxP Systems.
Thermo Fisher CRG ecosystem experience is highly desirable.

Responsibilities

Develop the enterprise roadmap for AI-driven software engineering.
Design the future-state AI-native SDLC operating model.
Lead enterprise-wide engineering transformation initiatives.
Create the business case for AI adoption including productivity improvements and ROI.
Develop maturity models for AI-enabled software engineering.
Create transformation metrics and executive dashboards.
Design an enterprise Agentic AI engineering architecture supporting various agents including Product Management, Business Analyst, Requirements Engineering, Architecture, UX Design, Software Engineering, Test Engineering, Security, Compliance, DevOps, Documentation, Release Management, and Site Reliability Agents.
Define orchestration patterns between autonomous agents.
Design Human AI collaboration workflows.
Develop governance for multi-agent systems.
Define the architecture for an enterprise AI Engineering Harness including LLM Gateway, Prompt Management, Context Management, RAG Platform, Knowledge Graph, Vector Database, MCP Servers, AI Memory, Workflow Orchestration, Agent Registry, Tool Registry, Policy Engine, Observability, Evaluation Framework, Model Gateway, Security Framework, Model Routing, and Enterprise Knowledge Integration.
Modernize software delivery practices using AI.
Introduce Spec-Driven Development, AI Pair Programming, Autonomous Code Generation, AI Code Reviews, AI Refactoring, AI Documentation, Automated Architecture Reviews, Engineering Knowledge Management, Agentic DevSecOps, Autonomous Test Generation, AI Performance Optimization, and AI Release Management.
Define enterprise reference architectures for AI Engineering Platform, Agentic Application Architecture, Developer Experience, Platform Engineering, Cloud Architecture, Microservices, API Strategy, Data Platforms, Event Architecture, Knowledge Architecture, and AI Infrastructure.
Ensure AI-enabled engineering complies with FDA, GAMP5, HIPAA, GDPR, EU Cyber Resilience Act, ISO 27001, SOC2, Clinical Software Validation, 21 CFR Part 11.
Develop AI governance and validation processes.
Define Responsible AI standards.
Develop audit-ready AI engineering practices.
Establish engineering standards for Coding, Architecture, Testing, Security, Documentation, Prompt Engineering, Agent Design, LLM Evaluation, Agent Evaluation, AI Safety, Knowledge Management, Reusable Components, Developer Experience.
Evaluate enterprise AI platforms including OpenAI, Azure AI, Anthropic, Google Gemini, AWS Bedrock, NVIDIA AI Enterprise, Ollama, LangGraph, CrewAI, AutoGen, Semantic Kernel, LangChain, PydanticAI, MCP, GitHub Copilot, Cursor, Windsurf, Claude Code, Amazon Q, Azure DevBox.
Lead enterprise AI adoption.
Coach engineering leaders.
Develop AI enablement programs.
Create AI engineering playbooks.
Develop training and certification.
Establish Communities of Practice.
Create engineering KPIs.
Measure AI adoption.