Principal, AI Engineer, Solution Delivery

Job Description

Must Have Technical/Functional Skills

Extensive experience applying data science, ML, and AI to real-world business problems, with demonstrated growth

in scope, complexity, and influence.

Fluent with AI-assisted engineering tools, such as Codex, Claude Code, used daily as a force multiplier, with critical

review of generated output. Experience building engineering agents that accelerate delivery itself, such as testing,

code review, or deployment-validation agents, not just using AI tools to write code.

Full-stack AI fluency: demonstrated ability to work across the spectrum from statistical modeling and ML to LLM

application development and agentic system design, including rapid prototyping (e.g., Streamlit, React) and owning

solutions through to production.

Strong software engineering skills in Python; working knowledge of SQL and JavaScript/TypeScript sufficient to build

and ship a solution's front end (e.g., React). Comfort with modern development practices including testing, code

review, and CI/CD. Working fluency with containers (Docker) and Kubernetes-based deployment, sufficient to

independently ship and operate a solution on the platform, not to build or own platform infrastructure.

Deep hands-on experience with LLMs, RAG architectures, and prompt engineering, including graph-based retrieval

(GraphRAG, knowledge graphs) alongside traditional vector-based RAG. Hands-on experience with multi-agent and

sub-agent orchestration frameworks (e.g., LangGraph, Google ADK, or equivalent) and MCP or equivalent

tool-integration protocols, for production agentic systems.

Experience developing and evaluating AI systems rigorously: automated evaluation pipelines, red-teaming,

hallucination detection, safety testing, and performance monitoring.

Proficiency with cloud AI platforms, particularly Google Cloud Platform Vertex AI for agentic development, with working knowledge of

AWS services (SageMaker, Bedrock) and modern data platforms (Databricks). Comfort consuming cloud-native

platform services (existing IaC templates, CI/CD pipelines, the AI gateway) to deploy and operate solutions.

Strong stakeholder engagement and communication skills: ability to scope initiatives, present to senior leaders,

manage expectations, and drive alignment across business and technology partners.

Track record of mentoring and elevating technical talent.

Experience in life insurance, financial services, or other regulated industries is a plus.

Roles & Responsibilities

Lead AI/ML and GenAI initiatives end-to-end, in partnership with data, technology, product, and business teams.

Scope and shape initiatives from opportunity identification and feasibility assessment through solution design,

delivery, and stakeholder alignment.

Own end-to-end delivery for AI solutions, from build through evaluation, deployment, and production monitoring,

shipping and operating them on the enterprise AI platform using its existing CI/CD, IaC templates, and gateway.

Design and implement agentic AI systems, including multi-agent orchestration, tool use, memory architectures,

human-in-the-loop checkpoints, and safety guardrails. Build multi-agent solutions with sub-agents that plan,

implement, validate, deploy, and log.

Design and deploy produc tion-grade RAG and retrieval systems, including hybrid search, reranking, evaluation, and

advanced retrieval patterns (agentic RAG, graph-enhanced retrieval) as appropriate.

Lead technical decisions across the AI stack: model selection, orchestration frameworks and tool-integration

protocols (e.g., LangGraph, MCP, or direct API integration), cloud AI platforms (Google Cloud Platform, AWS), and data platform

(Databricks) integration.

Rapidly prototype AI-powered applications and interfaces to validate ideas, test usability, and accelerate adoption,

using modern frameworks (Streamlit, React, or similar).

Build with AI-assisted engineering tools daily (such as Codex, Claude Code) as a force multiplier, with critical review

of all generated output. Build engineering agents that accelerate the team's own delivery, for example agents that

assist with testing, code review, or deployment validation, not just use AI tools to write code.

Define and evolve technical standards for AI development, including evaluation frameworks, testing practices,

observability, and responsible AI principles. Evaluate and integrate emerging AI capabilities (new foundation models,

agent frameworks, AI-assisted development tools).

Provide technical leadership and mentorship to data scientists and AI engineers, fostering a culture of rapid

experimentation, rigorous evaluation, and continuous learning.

This role involves limited travel (