3M is seeking an Agentic AI Architect to set the architecture, standards, and operating model for enterprise agent systems that plan, reason, and act via secure interfaces, to accelerate automation and decision support.
Requirements
- Advanced proficiency in Python and modern software engineering practices + agent frameworks such as LangGraph, AWS Strands or MS AutoGen.
- Experience with RAG/memory architecture (chunking, embeddings, vector DBs, enterprise search/knowledge graphs) and secure integration with SAP/Salesforce/Snowflake.
- Strong command of cloud architecture and governance on AWS or Azure encompassing network isolation, identity and secrets management, and data-protection controls; practical experience implementing AI safety and compliance (prompt-injection and jailbreak mitigation, PHI/PII handling, GDPR/SOC 2).
- Hands-on experience with cloud agent platforms (e.g., AWS Bedrock AgentCore, Azure AI Agent Service/AI foundry) and routing across multiple model providers.
- Familiarity with agent evaluation and risk controls, including offline/online task-based evaluations (e.g., Ragas, DeepEval), guardrail/policy engines, prompt-injection/jailbreak mitigation, and PHI/PII protection.
Responsibilities
- Own the reference architecture and roadmap for agentic AI, defining standards for multi-agent patterns, tool/function interfaces, RAG/memory design, orchestration, security, and environment segregation.
- Translate business objectives into a prioritized portfolio of agentic use cases; mentor engineers and data scientists; and communicate architecture decisions, risks, and value realization to executive stakeholders.
- Lead the design and delivery of production grade agentic solutions that safely execute actions in enterprise systems (e.g., SAP, Salesforce, Snowflake etc), with CI/CD, automated testing, rollback, and human-in-the-loop controls.
- Establish and enforce governance, safety, and compliance controls—including prompt injection/ jailbreak defenses, policy guardrails, data classification/residency, and PHI/PII handling—in partnership with Security, Legal, and Risk.
- Implement evaluation, observability and operations at scale: task-based evals, telemetry and tracing, incident response, and FinOps practices to achieve defined SLAs for reliability, latency, and cost.
Other
- Master’s in computer science, applied mathematics, or engineering field or a science field (completed and verified prior to start)
- Five (5) years of experience in AI engineering, with at least Two (2) years in a Generative AI, AI driven automation or RPA in a private, public, government or military environment.
- Travel: May include up to 10% domestic and international
- Must be legally authorized to work in country of employment without sponsorship for employment visa status (e.g., H1B status).
- Relocation Assistance: Is Authorized
