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Staff AI Engineer, Internal AI Engineering | Confidential Fintech A high-growth fintech is standing up a new Internal AI Engineering team with a mandate to build the agentic systems that transform how the company operates across sales, marketing, revenue ops, finance, talent, and legal. Reporting to the Head of New Product Engineering, you'll define what to build not just execute what's scoped. You'll be the first major internal consumer of the company's in-house agentic AI platform, using its primitives to ship fast while your real-world usage shapes its roadmap. What You'll Do <ul style="margin:0px;padding-left:40px;">
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Partner with business teams (Revenue Ops, Marketing, Finance Ops, Talent Acquisition) to catalog manual, high-frequency workflows and rank them by impact, feasibility and urgency</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Build multi-step, tool-augmented agents that plan, execute, observe and improve, using planner/executor and reflection-based architectures</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Develop stateful agent systems with human-in-the-loop controls: approval gates, fallback paths and escalation mechanisms</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Build on the internal agentic platform (runtime, orchestration, memory, tool registry, guardrails, observability) and contribute reusable patterns back</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Create differentiated internal solutions that can't be bought off the shelf, staying ahead of emerging AI technologies and regulatory frameworks</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Use the platform's eval harness and tracing to drive failure-mode analysis and continuous optimization</li>
</ul> Required <ul style="margin:0px;padding-left:40px;">
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">8+ years software engineering, including 2+ years on AI/ML systems or LLM-powered applications in production
</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Deep hands-on experience with LLM APIs and at least one agentic framework (LangGraph, CrewAI, AutoGen)</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Strong Python; production-grade backend services, APIs and data pipelines</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Proven ability to operate in ambiguity: you've walked into an undefined problem space, figured out what to build, built it and measured the results</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Shipped AI/automation that directly impacted business operations</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Strong systems thinking: you design for reliability, observability and maintainability from day one, not as an afterthought</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Comfortable translating non-technical stakeholder workflows into technical solutions</li>
</ul> Preferred <ul style="margin:0px;padding-left:40px;">
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Multi-agent systems, workflow orchestration or complex tool-use patterns in production</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Eval frameworks (LangSmith, W&amp;B, Arize or custom pipelines)</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">RAG pipelines, vector DBs, knowledge graphs or memory/grounding systems</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Real-time or streaming AI systems</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">AI safety and security: prompt injection prevention, hallucination mitigation, data privacy</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">Regulated industry background (fintech, healthcare, government)</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">MCP, function calling or similar structured tool integration</li>
<li style="line-height:1.2;font-family:Arial;font-size:14px;color:rgb(0, 0, 0);margin:0px;">AWS LLM infrastructure (Bedrock, AgentCore/Strands, Lambda, SageMaker)</li>
</ul>

Staff AI Engineer, Internal AI Engineering | Confidential Fintech

A high-growth fintech is standing up a new Internal AI
Engineering team with a mandate to build the agentic systems that
transform how the company operates across sales, marketing,
revenue ops, finance, talent, and legal.

Reporting to the Head of New Product Engineering, you'll define
what to build not just execute what's scoped. You'll be the first
major internal consumer of the company's in-house agentic AI
platform, using its primitives to ship fast while your real-world
usage shapes its roadmap.

What You'll Do

* Partner with business teams (Revenue Ops, Marketing, Finance
Ops, Talent Acquisition) to catalog manual, high-frequency
workflows and rank them by impact, feasibility and urgency
* Build multi-step, tool-augmented agents that plan, execute,
observe and improve, using planner/executor and reflection-based
architectures
* Develop stateful agent systems with human-in-the-loop controls:
approval gates, fallback paths and escalation mechanisms
* Build on the internal agentic platform (runtime, orchestration,
memory, tool registry, guardrails, observability) and contribute
reusable patterns back
* Create differentiated internal solutions that can't be bought
off the shelf, staying ahead of emerging AI technologies and
regulatory frameworks
* Use the platform's eval harness and tracing to drive
failure-mode analysis and continuous optimization

Required

* 8+ years software engineering, including 2+ years on AI/ML
systems or LLM-powered applications in production

* Deep hands-on experience with LLM APIs and at least one agentic
framework (LangGraph, CrewAI, AutoGen)
* Strong Python; production-grade backend services, APIs and data
pipelines
* Proven ability to operate in ambiguity: you've walked into an
undefined problem space, figured out what to build, built it and
measured the results
* Shipped AI/automation that directly impacted business
operations
* Strong systems thinking: you design for reliability,
observability and maintainability from day one, not as an
afterthought
* Comfortable translating non-technical stakeholder workflows
into technical solutions

Preferred

* Multi-agent systems, workflow orchestration or complex tool-use
patterns in production
* Eval frameworks (LangSmith, W&B, Arize or custom pipelines)
* RAG pipelines, vector DBs, knowledge graphs or memory/grounding
systems
* Real-time or streaming AI systems
* AI safety and security: prompt injection prevention,
hallucination mitigation, data privacy
* Regulated industry background (fintech, healthcare, government)
* MCP, function calling or similar structured tool integration
* AWS LLM infrastructure (Bedrock, AgentCore/Strands, Lambda,
SageMaker)

Senior AI Engineer x 6 openings

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