Principal AI Engineer

Description

Role Summary

The Principal Agentic AI Engineer owns the technical direction for a cluster of business domains, delivering automation and agentic AI systems from design through production, and mentoring other engineers. This is a hands-on, high-accountability role that combines software engineering depth with genuine enthusiasm for building AI-native automation.

You are a technical lead and set the tone for engineering quality, drive architectural decisions within your domain, and are the person engineers look to when they are stuck. You work closely with the Product Owner to ensure work is well-scoped and with the Engineering Manager to surface problems early and meet committed delivery timelines.

This function is building toward production agentic AI. You do not need to have shipped a production agent - but you need to understand what that means, have the foundations to get there, and be excited about the journey.

Essential Functions

• Own the technical direction of multiple automation and AI workstreams in partnership with AI Architect and other Senior Agentic AI Engineers - architecture decisions, tooling choices, and build quality.
• Deliver complex automation and agentic AI systems from design through production deployment, including monitoring and post-deployment review.
• Use RAG pipelines to connect agents to internal knowledge sources, ensuring accuracy and quality of agent responses.
• Develop and enforce guardrails, safety layers, and human-in-the-loop verification patterns for agent outputs in a regulated financial services environment.
• Build and maintain agent evaluation frameworks - moving beyond ad hoc testing toward systematic quality measurement, regression testing, and production observability for AI systems.
• Mentor senior and mid-level engineers - code reviews, pairing, and active investment in their growth as engineers.
• Partner with the Product Owner on discovery - helping scope automation opportunities technically, identify feasibility risks, and estimate effort honestly.
• Develop and version structured prompt engineering standards - system prompts, output contracts, and verification rules - treating prompt design as an engineering discipline.
• Contribute to knowledge sharing - rapidly bringing patterns and lessons learned into the broader IA engineering community.

Responsibilities:

• Delivers automation and agentic AI systems independently, end-to-end, from discovery through production
• Designs and maintains RAG pipelines, agent evaluation harnesses, and guardrails for production AI systems
• Mentors senior and mid-level engineers - active, ongoing investment in their development
• Partners with PO on discovery and scoping - technically credible contributor to the prioritization process
• Drives AI-assisted development adoption
• Develops and enforces prompt engineering standards and agent safety patterns

Technical Capabilities:

• Strong Python engineering skills - automation scripting, API integration, data processing, and production-quality code
• Hands-on experience with LLM APIs (Claude, OpenAI, or equivalent) and agentic AI patterns - tool calling, orchestration, evaluation
• Experience with agent orchestration frameworks (LangChain/LangGraph, CrewAI, n8n, or similar) - ability to evaluate tradeoffs and select appropriate tooling for the use case
• Experience building RAG pipelines using vector databases (Pinecone, pgvector, Weaviate, Chroma, or similar)
• Proficiency in structured prompt engineering - versioned prompts, system prompt design, output contracts, and evaluation-driven prompt iteration
• Familiarity with Model Context Protocol (MCP) for agent-tool integrations, or demonstrated ability to learn tool-use standards quickly
• Experience with cloud platforms (AWS, Azure, or GCP) for deploying and operating AI systems
• Understanding of production requirements for AI systems - monitoring, drift detection, failure modes, and human-in-the-loop design
• Comfortable with modern CI/CD practices and deployment pipelines
• Familiarity with RPA tooling (UiPath or similar) - does not need to be a primary skill but must be able to contribute

Technical Competencies:

• 8+ years of software engineering experience, with at least 1-2 years working on automation, AI, or data engineering systems
• Demonstrated experience delivering software to production - not just prototyping
• Experience mentoring or providing technical leadership to less experienced engineers
• Equivalent experience accepted in lieu of formal degree
• Experience in a financial services or regulated environment a plus
• Experience with agent observability tools (LangSmith, Arize, Langfuse, or similar) a plus
• Familiarity with guardrails frameworks (NVIDIA NeMo Guardrails, Guardrails AI, or custom safety layers) a plus

Behavioral Attributes:

• Builds pod confidence through technical clarity - engineers in the pod always know what good looks like and how to get there
• Promotes member experience as the measure of automation quality
• Shows initiative in identifying automation opportunities beyond what the backlog already contains
• Welcomes feedback and models the growth mindset expected of senior and mid-level engineers on the team
• Open and energized by the pace of change in agentic AI - treats uncertainty as a feature, not a bug
• Generous with knowledge - documents patterns, shares learnings, and contributes to the team's collective intelligence

Location: Marlborough/Chelmsford MA or Hillsborough, OR

Target Compensation: $164,000 -$197,000

Schedule: 8am- 5pm