Lead Agentic AI Engineer

What is the opportunity?

The Agent Lead sits at the forefront of transforming Financial Advisor productivity through agentic AI workflows—bridging business problems with intelligent automation. This high‑ambiguity, rapid‑experimentation role defines how vendor agents, enterprise frameworks, and internally developed agents coexist and interoperate within a governed ecosystem. It is a product‑oriented builder role that shapes, validates, and scales agentic patterns reusable across Wealth Management.

• Partner directly with Financial Advisors, field leadership, and business stakeholders to identify high‑value agentic workflow opportunities and evaluate when AI is the right solution versus deterministic automation.
• Lead rapid POC development cycles with authority to “fail fast / scale fast,” designing multi‑agent interactions across vendor agents (CRM/Agentforce), enterprise agents, and native/internal agents.
• Act as Product Owner for agentic workflows—owning use‑case shaping through validated solution patterns, defining success metrics, and driving iteration based on advisor feedback and usage telemetry.
• Establish reusable agent design patterns including prompting strategies, orchestration, memory models, tool usage, and escalation paths in collaboration with AI Engineering.
• Engage with enterprise stakeholders (Borealis, architecture, and platform teams) to align with approved agentic frameworks, standards, and governance requirements.
• Manage and develop Context Engineers/Prompt Engineers, establishing best practices in context design, retrieval strategies, and agent behavior tuning.
• Travel (~25%) to branches and field locations to observe advisor workflows, identify friction points, validate usability, and drive adoption of agentic solutions.

• 8‑10 years total engineering experience, with 2‑3+ years specifically building agentic or LLM systems (not just prototypes).
• Hands‑on RAG architecture—chunking trade‑offs, retrieval failures, evaluation.
• Built or extended tool integration layers connecting LLM agents to external systems.
• Strong Python backend skills—FastAPI, async, Pydantic, streaming responses.
• Proven experience building and deploying agentic AI solutions (multi‑agent systems, orchestration frameworks, tool‑using agents).
• Strong understanding of agent frameworks, architectures, memory models, tool integration, and event‑driven agents.
• Demonstrated ability to operate as a builder + product owner hybrid with strong judgment on when to use AI versus non‑AI solutions.
• Experience designing workflow‑driven automation and working across business, engineering, and enterprise governance functions.
• Leadership experience managing technical talent and excellent stakeholder engagement skills for “side‑of‑desk” collaboration.

• Experience in Wealth Management or Financial Services, particularly with advisor workflows.
• Familiarity with CRM‑based agent platforms (Salesforce Agentforce) and event‑driven architectures.
• Understanding of AI risk, model governance, explainability frameworks, and human‑centered design.

• A comprehensive total‑rewards program including competitive compensation and flexible benefits such as a 401(k) with company matching, health, dental, vision, life, disability insurance, and paid‑time off.
• Leadership support through coaching and management opportunities.
• Opportunity to make a lasting impact and build close relationships with clients.
• Work in a dynamic, collaborative, progressive, and high‑performing team.
• Chance to take on challenging work and pursue professional growth.

Expected salary range: $100,000–$170,000, depending on experience, skills, and market conditions.

Minneapolis, United States.