Triad Financial Services is a leading provider of financial services and solutions, serving clients nationwide and seeking an AI Lead Engineer or Manager who will report directly to the Chief Information Officer. This role requires an experienced strategic leader who will be responsible for developing and executing the enterprise-wide AI vision, strategy, and roadmap. This role ensures the protection of digital assets, data, and infrastructure surrounding our AI posture.

This role partners with Technology, Risk, Compliance, and business stakeholders to identify high-value use cases, deploy copilots/agents with appropriate guardrails, and ensure solutions remain reliable, secure, and compliant in production. The role balances innovation with governance, emphasizing human-in-the-loop oversight for higher-impact use cases and safe handling of sensitive information.

Key Responsibilities

1. Agentic AI & Automation Delivery

Lead the design and implementation of agentic AI solutions that execute multi-step workflows under defined guardrails (e.g., document extraction/processing pipelines, workflow automation, task orchestration).

Translate business problems into deployable AI solutions, including solution definition, integration approach, and rollout plan; coordinate cross-functional delivery with engineering, product, and business teams.

Drive hands-on implementation where needed (prototyping, prompt/workflow orchestration, integration patterns), while establishing repeatable delivery playbooks.

2. End-User Productivity Enablement

Identify and prioritize high-impact productivity opportunities where AI can reduce cycle time, minimize errors, and increase throughput across teams.

Develop adoption patterns for copilots/agents embedded in day-to-day work (communications, knowledge workflows, meeting productivity, task management), ensuring the experience is easy, safe, and measurable. (Microsoft's direction is that agents are embedded directly in Microsoft 365 apps and Copilot experiences.)

Partner with business leaders to define "what good looks like" for productivity (e.g., time saved, task completion, quality improvements) and to build continuous feedback loops.

3. Governance, Risk, and Controls (Responsible AI)

Implement and enforce AI governance aligned to internal policy, including definitions and requirements for agentic AI, human-in-the-loop, and treatment of sensitive information.

Define AI risk controls and vendor/solution due diligence using structured evaluation criteria (e.g., prompts/logging access, authorization boundaries, explainability, incident response, retention).

Establish lifecycle controls for AI systems in production: monitoring, drift detection, retraining triggers, and quality gates.

Partner with Risk Management and related teams to define and manage AI-based risks and mitigation strategies.

Build "agent observability" practices and operational dashboards; industry signals indicate this role increasingly centers on monitoring/scorecards and agent performance management.

4. Data, Quality, and Validation

Own the testing and validation strategy for AI outputs and automation pipelines (accuracy, bias/fairness checks where applicable, reliability and repeatability), including SQL-based validation and backend testing as needed.

Ensure data integrity and the safe handling of regulated/sensitive data within AI workflows.

5. Stakeholder Leadership & Program Management

Run an AI intake and prioritization process (use case discovery → feasibility → risk review → pilot → scale).

Communicate AI progress, risks, and outcomes with clarity; produce periodic executive updates on adoption, trends, and measurable value. (Your internal artifacts already emphasize structured AI journey reporting.)

Minimum Qualifications:

- Bachelors or Master's in Computer Scient, AI, Data Science, or related field - 7+ years of AI/ML experience, including enterprise deployments; - 5+ years in a leadership role (people leadership and/or program leadership) - Hands-on experience deploying AI integrations and automation in enterprise environments; strong understanding of LLMs, NLP, and agentic AI frameworks - Demonstrated experience with AI governance, security, bias mitigation, and human-in-the-loop systems. - Strong stakeholder management and communication skills, including partnering with risk/compliance

Preferred Qualifications: - Experience with Microsoft productivity/AI ecosystem (e.g., Microsoft Copilot) and enterprise automation tooling. - Experience in mortgage, financial services, or similarly regulated environments. - Experience evaluating AI vendors and solutions using structured scoring/risk matrices, including authorization controls and prompt/retention considerations. - Familiarity with emerging industry practices for agent governance/security across data governance, observability, and lifecycle controls.

Key Skills & Competencies:

- Agentic workflow design (multi-step tasks, tool use, guardrails) - Production operations for AI: monitoring, drift detection, validation frameworks - Responsible AI & compliance mindset; ability to translate policy into technical controls - Cross-functional leadership (Technology + Business + Risk/Compliance)

Physical Demand:

- Ability to safely and successfully perform the essential job functions consistent with the ADA, FMLA and other federal, state and local standards, including meeting qualitative and/or quantitative productivity standards. - Ability to maintain regular, punctual attendance consistent with the ADA, FMLA and other federal, state and local standards. - Must be able to talk, listen and speak clearly on the telephone.