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You will build and integrate LLM-powered features and agent workflows into a B2B SaaS product. Responsibilities include managing the full lifecycle from prototype to production, including evaluation, logging, and performance optimization.
Client: mid-size US startup (Series B, about 150 people) ·
Location: remote, LATAM, 4+ hours overlap with US Eastern ·
Level: mid
Engagement: full-time contractor
You will ship LLM-powered features into a B2B SaaS product, working with product engineers from prototype to production.
What you will do
Build retrieval-augmented generation pipelines and agent workflows on top of commercial and open-source LLMs
Write and version prompts, tools and evaluation sets
Integrate AI features into the product's Python and TypeScript services
Measure quality, latency and cost, and improve them release over release
Add guardrails, logging and tracing for production use
What you bring
3+ years of software engineering, at least 1 building LLM applications in production
Strong Python; experience with an LLM framework or SDK (LangChain, LlamaIndex or direct provider APIs)
Vector databases (pgvector, Pinecone or Weaviate) and embedding-based search
API design, testing and CI/CD habits
Advanced English
Nice to have
Evaluation tooling (LangSmith, Braintrust, Ragas)
TypeScript and React
Fine-tuning or classical ML background
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