Staff AI Engineer

Boulder, CO, Charlotte, NC or Remote

WHO WE ARE

Our client builds the AI operating layer for wealth. Our platform delivers agentic workflows across the industry’s core personas – investors, advisors, investment teams, and operations – so financial institutions can move faster, serve more clients, and deliver better outcomes with the same (or fewer) resources. We combine finance-native AI, specialized data, and enterprise-grade controls to deploy secure, compliant capabilities into real production environments.

WHAT SETS US APART

• Speed: We build and ship quickly—MVPs in ~3 months, production-ready products in ~6–12 months.
• Track Record: Prior exits include 55ip (acquired by J.P. Morgan) and Paralel Technologies
• Strategic Partners: Partners include J.P. Morgan, SEI, Franklin Templeton, Morningstar, Broadridge, Motive Partners and Tectonic Ventures.
• World-Class Team: Complimentary expertise across AI and financial services, with experience from Google, Microsoft, Uber, PayPal, eBay, BlackRock, LPL, Franklin Templeton, Morgan Stanley, Broadridge and more. 

OUR VALUES

• Grow at the Edge. We are driven by personal growth fueled by a beginner’s mindset. We get out of our comfort zone and keep egos aside. With self-awareness and integrity we strive to be the best we can possibly be. No excuses.
• Understanding through Listening and Speaking the Truth. We communicate with authenticity, precision and integrity to create a shared understanding. We identify opportunities within constraints and propose solutions in service to the team.
• I Win for Teamwin. We believe in staying within our genius zones to succeed and taking accountability for driving results. We are all individual contributors first and always thinking about what can be better.

ROLE OVERVIEW

As we scale our AI platform, we are building production-grade multi-agent systems that power advisor copilots, investment intelligence workflows, and autonomous research capabilities. We are seeking a Staff AI Engineer to architect, build, and operationalize these systems at scale. This is a hands-on, high-impact engineering role focused on shipping reliable, enterprise-ready agentic AI systems into production.

PROJECTS

• Advisor Copilot (Multi-Agent Systems)
  Build a production-grade, multi-agent copilot for financial advisors that retrieves and reasons over client data, analyzes portfolio exposures and risk scenarios, generates personalized insights, enforces compliance guardrails, and drafts client-ready communications — all within a monitored, auditable architecture.
• Workflow Automation
  Design end-to-end AI workflows spanning client discovery, investment research synthesis, portfolio construction and optimization, and compliant meeting preparation — replacing fragmented tools with intelligent, autonomous systems.
• AI Agent Platform & Infrastructure
  Architect a scalable multi-agent platform with orchestration engines, memory and state management, dynamic tool invocation, structured output validation, observability, fault tolerance, and automated evaluation — solving reliability, explainability, and regulatory challenges at scale.

WHAT YOU’LL DO

• Design and implement production-grade multi-agent systems using modern agent frameworks (e.g., Pydantic AI, Agent Harness, Tool-Calling, Code Execution)
• Build agent workflows that integrate context retrieval, reasoning, tool execution, validation, and compliance checks, 
• Develop distributed services for agent execution with strong observability, monitoring, and failure handling
• Establish evaluation frameworks for multi-step reasoning accuracy, groundedness, hallucination mitigation, and financial correctness
• Implement memory management, context handling, and agent state persistence strategies
• Partner with product, design, and engineering teams to translate business requirements into robust agent architectures
• Optimize systems for latency, cost efficiency, and reliability in production
• Contribute to infrastructure decisions around model serving, vector databases, caching, and orchestration layers

WHAT YOU’LL BRING

• 3+ years of experience building and shipping Generative AI and LLM applications into production, 6+ years of ML experience.
• Demonstrated experience designing and deploying multi-agent systems of various architecture
• Strong experience with multimodal LLMs, knowledge graph, data synthesis, LLM fine tuning, reinforcement learning, agent harness, agent memory
• Deep proficiency in Python and modern AI frameworks
• Experience with distributed systems, cloud infrastructure (AWS/GCP/Azure), and containerized deployments
• Experience implementing monitoring, evaluation, and reliability safeguards for AI systems
• Strong systems thinking — ability to design beyond single-model solutions toward coordinated, multi-component architectures
• Resilience and adaptability – experience working at early-stage startups is a plus