Key skills
AWSDockerEC2JavaScriptNext.jsPostgresPythonReactRedisSQLTypeScriptAIMLOpenAIClaudeAnthropicGeminiRAGLangChainAgenticLangGraphQdrantPineconeWeaviateTailwindshadcnshadcn/uiBunFastAPItRPCVitestPlaywrightUnit TestingGitHub ActionsS3GlueVertex AIPostgreSQLDrizzleGitHubCloudflareCachingCI/CD
About this role
Role Overview
- Multi-step AI agents that handle complex, real-world workflows end to end
- RAG pipelines that actually perform — not just LangChain defaults
- Durable background workflows for processing, enrichment, and orchestration
- Internal dashboards, admin tools, and integration surfaces (Slack, Discord, Intercom)
- Eval harnesses to measure and improve model performance over time
- The glue layer between LLMs, APIs, databases, and product features
Requirements
- Python 3.12 as your primary language — you live in the AI/ML ecosystem; FastAPI for AI/agent service APIs
- TypeScript (strict) in a monorepo on Bun + Turborepo — Next.js (App Router), React, Tailwind, shadcn/ui for UI; Hono + tRPC for the API layer; Better Auth for authentication
- Comfortable with SQL and Drizzle ORM with PostgreSQL — comfortable querying and working with a relational data model
- Hands-on with Anthropic, OpenAI, and Google Vertex AI (Gemini) APIs — function/tool calling, structured outputs, prompt caching; multi-provider failover via OpenRouter and Cloudflare AI Gateway
- Production experience with an agent framework: LangGraph for agent/AI services — not just a weekend project
- Familiar with Model Context Protocol (MCP) — building and consuming MCP clients/servers to extend agent capabilities
- You know the boring-but-critical stuff: streaming, retries, token cost management, rate limiting
- Experience with vector DBs: pgvector (preferred), Pinecone, Weaviate, or Qdrant
- Intentional about embedding models and chunking strategy — you've reasoned about trade-offs, not just accepted defaults
- Implemented hybrid search (BM25 + vector) when precision matters
- Code-first workflow experience: BullMQ + Redis for queues, scheduled jobs, and durable background orchestration
- Aware of low-code tools: n8n, Zapier, Make — can use them when appropriate
- Background jobs, queues, and scheduled tasks are second nature
- Active user of at least one eval/observability platform: Langfuse (self-hosted) for tracing and evals — our primary observability platform; familiarity with LangSmith, Braintrust, or Helicone is a plus
- You've actually run an eval set — you have opinions on what makes a good one
- Claude Code is your daily driver — plus comfort operating within a homegrown agentic-dev layer (hooks, skills, subagents)
- You ship internal tools in days, not sprints
- You have a 'vibe coding' instinct — but you don't let it erode eng discipline
- Comfortable deploying on AWS (EC2, S3, SES, ECR, Parameter Store) in us-east-2; Docker / Docker Compose for dev and prod; Caddy for TLS and reverse proxy; GitHub Actions + OIDC for CI/CD
- Docker / Docker Compose, env management, and secrets handling (AWS Parameter Store) are table stakes
- Some Biome, Vitest, and Playwright familiarity is a plus for linting, unit testing, and end-to-end testing
- Can spin up Next.js (App Router) / React dashboards and admin interfaces when needed
- Familiar with Discord.js, Slack API, and Intercom API; comfortable with Asana, Fellow, and Google Workspace (Drive/Calendar/Gmail) integrations
- Understands product instrumentation with Mixpanel or GA4
Tech Stack
- AWS
- Docker
- EC2
- JavaScript
- Next.js
- Postgres
- Python
- React
- Redis
- SQL
- TypeScript
Benefits
- You'll work on a real, fast-moving product.
- AI is a first-class priority, not an afterthought.
- Small team, high ownership.
- The domain is genuinely interesting.
- We use the tools we ask you to know.