Director of Applied AI Engineering

NorthAB LLC•Remote work MI, MI

1d•$207,000 - $290,000•Remote

About The Position

The Director of Applied AI Engineering will build and lead North's Applied AI team: a hands-on group that takes AI capabilities from prototype to production inside real business workflows. This is a delivery role, not a research role, measured by systems that are live, adopted, and safe to run in a regulated financial services environment. This will require an individual with strong software engineering fundamentals, judgment about where large language models and machine learning outperform conventional automation, attention to detail, and the capacity to rapidly develop and act upon in-depth knowledge of our business, products, customers, and regulatory obligations. This position will partner with Product, Sales, Marketing, Operations, and Risk to improve current business processes, and will work with Risk, Compliance, Legal, and Information Security to ensure AI systems are explainable, auditable, and safely governed. This role is responsible for providing updates to leadership on the team's impact through clear reporting and KPIs, and for establishing the long-term strategy, technical architecture, and responsible-AI guardrails as well as the short-term scope and sequencing that gets value into production quickly.

Requirements

Bachelor's degree in Computer Science, Engineering, or related field, or equivalent work experience
10+ years of experience in software engineering, machine learning, or a related domain, including time in a customer-facing or business-embedded engineering capacity (ex. forward deployed, solutions, or professional services engineering)
2+ years building and shipping production systems on top of large language models (LLMs), including at least one agentic or conversational system live with real users
4+ years of hands-on management experience with a track record of hiring and growing teams as well as providing coaching and mentoring
Exceptional leadership skills, and proven ability to operate in a fast-moving environment
Hands-on approach to closing gaps in technical execution, able to personally debug a misbehaving agent in production
Demonstrated problem-solving and executive-level communication skills, bridging technical teams and leadership seamlessly.
Hands-on expertise in code-level AI SDKs and agent frameworks (e.g., LangGraph, CrewAI, AWS Strands Agents), selecting optimal tools rather than relying on no-code builders.
Knowledge of core building blocks of production AI applications: tool/function calling, RAG, vector search, structured outputs, conversation and state management, and the judgment to favor deterministic workflows when appropriate.
Cloud engineering skills (preferably AWS), including deploying agents on managed services (e.g., Bedrock AgentCore), containers, IAM, networking, and cost optimization.
Track record deploying and maintaining production AI systems, driving robust CI/CD, tracing, incident response, and latency/cost controls.
Experience making evaluation part of the build cycle, including test sets, regression testing, human review, and measuring quality once systems are live
Grasp of AI security and guardrails (prompt injection defense, least-privilege access, audit trails, and human-in-the-loop design) aligned with financial regulations (Model Risk Management, SOC 2, PCI DSS, GLBA).

Responsibilities

Build and lead a high-performing Applied AI engineering team, defining the practice's operating model, engagement strategy, and technical standards from the ground up.
Partner with Product, customers, and business stakeholders to observe workflows and turn operational challenges into effective AI solutions.
Drive rapid discovery, prototyping, and seamless transition of AI capabilities to production across concurrent enterprise projects.
Architect, deploy, and maintain production AI solutions, including agentic workflows, chatbots, copilots, agent-assist tools, and traditional ML models.
Establish modular, reusable integrations for AI agents to securely access internal systems and enterprise data with least-privilege controls.
Own the AI evaluation lifecycle, incorporating offline/online evals, benchmark datasets, human-in-the-loop validation, and production readiness criteria.
Manage production operations alongside Cloud and Data engineering, overseeing CI/CD, monitoring, incident response, latency, and inference cost optimization.