Engineering Manager AI Agentic Enablement (AI)

Engineering Manager AI Agentic Enablement

Company

Reap

Conditions

6 days ago

Skills

Candidate Availability

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

You will lead the development of production AI capabilities, including agentic data layers, operational workflows, AI infrastructure, and governance. You will set technical direction, contribute to code and design reviews, partner with operational stakeholders, and lead a senior engineering team.

Requirements

• 8+ years of software engineering experience
• 2+ years in technical leadership or management
• Experience designing, deploying, and driving adoption of production AI or agentic systems
• Proficiency with LLM APIs, AWS Bedrock, MCP or equivalent agent tooling, Python or TypeScript orchestration, and workflow engines such as n8n
• Understanding of LLM failure modes, evaluation, guardrails, and human-in-the-loop controls
• Systems thinking across discovery, design, build, deployment, and optimization
• Ability to work with non-technical operators and translate workflows into buildable solutions
• Judgment on data classification, access control, and audit requirements in regulated finance
• Code quality, reliability, and peer-review expertise
• Clear communication and respectful challenge of ideas
• Engineering fundamentals with TypeScript, Node.js, NestJS, and AWS

Responsibilities

• Lead the design, security model, and roadmap for the agentic data layer
• Define secure, authenticated, permissioned, rate-limited, and auditable MCP servers and agent tools
• Build evaluation and observability for AI answer quality, tool correctness, latency, and failures
• Redesign operational workflows for agent execution with human approval checkpoints
• Translate operational procedures into scoped agentic workflows
• Design auditable workflows with error handling, retries, rollbacks, audit trails, and escalation paths
• Own the internal AI platform, including model access, data handling, deployment, and secure hosting
• Establish AI governance, risk classification, access controls, and logging standards
• Set standards for handling customer PII and regulated data
• Build AI skills, tooling, documentation, and paved-road workflows for engineers
• Hire, grow, and lead a senior engineering team
• Report operational impact through review time, throughput, cycle time, and returned capacity

Benefits

• Flexible hybrid/remote work environment
• Insurance coverage after probation
• Reap Card stipend
• Use of AI tools at work

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