Senior Full Stack AI Engineer
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Overview
Job Description
Senior Full Stack AI Engineer Minneapolis, MN | 6 - 12 years of experience
Job Description Senior Full Stack AI Engineer
Required Qualifications
- 8+ years of experience in software engineering, automation, or platform engineering roles.
- Proven hands-on experience implementing LLM based or agentic AI solutions in enterprise environments.
- Demonstrated expertise with Anthropic Cloud Code and advanced Claude services (including interpreter style workflows).
- Strong understanding of agent architectures, tool orchestration, and secure AI platform design.
- Experience working with investment, research, or Portfolio Management business users.
- Proficiency in Python and modern cloud native development patterns.
Required Qualifications
- Experience building AI platforms or shared AI services in financial services or regulated environments.
- Familiarity with portfolio management, investment research, or quantitative analytics workflows.
- Experience integrating AI solutions with enterprise data platforms and analytics stacks.
- Exposure to Responsible AI frameworks, model governance, and audit requirements.
Roles & Responsibilities We are seeking a Senior AI Automation Engineer to mature and expand our enterprise AI Platform AgentCore by designing, building, and scaling new AI driven capabilities that directly support Portfolio Management and Research workflows. This role will serve as a hands on technical leader responsible for operationalizing Anthropic Cloud Code and advanced Claude services (including Claude Interpreter) across the client delivering secure, governed, and reusable AI components. The engineer will work closely with Portfolio Managers, Research Analysts, and Quant teams to translate high value investment use cases into production grade agentic solutions deployed on AgentCore.
AI (AgentCore) Platform Maturity
- Design agentic workflows and multi agent orchestration patterns using approved AgentCore frameworks and runtimes.
- Build AI capabilities that leverage:
- Tool calling
- Retrieval augmented generation (RAG)
- Structured data semantics
- Reusable prompt and skill
Value Realization & Platform Adoption
- Demonstrate measurable impact on research productivity, decision velocity, and analytical depth.
- Support adoption via reference implementations, documentation, and enablement materials.
- Provide architectural guidance to teams building on AgentCore to prevent fragmentation and "shadow AI.
Enterprise Platform Enablement
- Integrate AI automation into existing enterprise platforms (e.g., CI/CD pipelines, SDLC tooling, cloud platforms).
- Establish reusable AI components, frameworks, and guardrails for product and engineering teams.
- Enable adoption through reference architectures, implementation patterns, and developer enablement.
Value Realization & Measurement
- Identify and quantify productivity, quality, and cycle time improvements driven by AI.
- Define KPIs and success metrics tied to SDLC efficiency, developer experience, and risk reduction.
- Support executive visibility into AI driven outcomes and maturity progress.
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