Applied AI Engineer
Indexed description
Our approach is different from most enterprise companies utilizing AI. Instead of sprinkling LLMs into every feature, we give the model scoped access to Chainalysis's data and product capabilities and let it write and execute code on behalf of the user in sandboxes with tenant isolation, scoped credentials, and full auditability. The user describes what they need; the system figures out the procedure. That turns every customer and every person in the organization into a problem solver, able to surface insights, build investigation graphs, generate reports, and automate workflows that previously required both domain expertise and programming ability. We're building a system that can extend itself, where new capabilities don't always require new code from us, because the agent can compose what already exists to solve problems no one anticipated.
You'll work across every layer of the platform. That means designing how agents discover and use capabilities across Chainalysis's product suite, building the sandboxed environments where agent-generated code executes with tenant isolation and scoped credentials, writing the skill libraries that give agents structured access to blockchain investigation and compliance data, and shipping the interfaces where users interact with results. You'll shape how agents solve problems based on user intent, and you'll own what you ship from design to production operation.
In This Role, You’ll
- Design and build the agent platform end to end, from the context and tool layer that shapes what agents can reason about, through the execution runtime where code runs, to the interfaces that expose results to users.
- Own the skill architecture, designing how agents discover, learn about, and invoke capabilities across Chainalysis's product suite. This includes writing skill client libraries, their documentation, and testing them end to end.
- Build and operate sandboxed execution environments where agent-generated code runs with tenant-level security boundaries and durable file persistence.
- Ship full-stack features across frontend, backend, skill libraries, and infrastructure. You own the full lifecycle from design through production operation.
- Develop the workflow platform that lets users automate complex processes beyond single conversations.
- Drive AI quality, cost, and observability, including multi-model orchestration, context window management, prompt engineering, and tracing across multi-step agent runs.
- Raise the reliability and security bar for systems where AI acts on behalf of users.
- Built and shipped full-stack features across frontend, backend, and infrastructure, with demonstrated ownership from design through production operation.
- Worked with LLMs in production and formed real opinions from it. Tool use, agent orchestration, streaming, prompt engineering, or retrieval-augmented generation. You've hit the failure modes and learned from them.
- A working understanding of how to structure what an agent knows and can do within the constraints of a context window. You've thought about the tradeoffs between baking knowledge into prompts, exposing tools, and letting agents write code against documented APIs.
- Deep backend engineering fundamentals in a typed language: concurrency, error handling, state management, and system design.
- Built or operated systems with real security constraints, tenant isolation, scoped credentials, sandboxed execution, or similar patterns where trust boundaries matter.
- Delivered frontend experiences with a modern stack, component architecture, data fetching, and real-time streaming.
- Operated what you've built. You've been on-call, debugged production incidents, and improved the reliability of systems you own.
- Designed tool interfaces or capability systems for AI agents, skills, function schemas, or similar patterns for giving models structured access to external systems
- Worked with the Effect ecosystem or strongly-typed functional programming in TypeScript
- Built multi-tenant platforms or systems with enterprise security requirements
- Experience in Python library development or designing SDKs for programmatic consumption
- Worked within Blockchain, compliance, or financial services domains
- TypeScript, Effect, Node.js
- React, TanStack Router, TanStack Query, Tailwind, shadcn
- Various AI Frameworks (Vercel AI SDK, Agent SDK, OpenCode)
- Python
- PostgreSQL
- AWS Lambda, S3, STS, CloudWatch, ECR
- OpenTelemetry
- Docker, pnpm, Turborepo, Vitest
- Amazon Bedrock, Google Vertex AI
You belong here.
At Chainalysis, we believe that diversity of experience and thought makes us stronger. With both customers and employees around the world, we are committed to ensuring our team reflects the unique communities around us. We’re ensuring we keep learning by committing to continually revisit and reevaluate our diversity culture.
We encourage applicants across any race, ethnicity, gender/gender expression, age, spirituality, ability, experience and more. If you need any accommodations to make our interview process more accessible to you due to a disability, don't hesitate to let us know. You can learn more here. We can’t wait to meet you.
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