AI/LLM Engineer — Python, LangChain, RAG, AWS Lambda (General Rodríguez)
OneSeven Tech (OST)
- General Rodríguez, Provincia de Buenos Aires
- Permanente
- Tiempo completo
We're building AI-powered products, primarily leveraging off-the-shelf LLM APIs, while also partnering with our Applied Science teams to integrate custom models when needed. You'll own end-to-end development of AI features — from prototyping to production — focused on reliability, accuracy, and scalable delivery.
Must-HavesStrong Python engineering skills and experience delivering production systems
Hands‑on experience building with LLM APIs (OpenAI, Anthropic, etc.) and deploying AI features end‑to‑end
Experience with LangChain / LangGraph — including Prompt Engineering, Token Management, and Agent creation with tool‑based workflows
Experience building agentic workflows (tool use, routing, memory, safety/guardrails), ideally with MCP
Solid understanding of RAG concepts — retrieval, chunking, embeddings, re‑ranking, and evaluation
Practical AWS experience, especially AWS Lambda and serverless architecture patterns
Backend engineering experience: RESTful API design, webhook integration, JWT, pytest, and observability tools like Sentry
Experience doing Git repository analysis — understanding large codebases, debugging, refactors, and code health
Comfortable working in fast‑moving product environments with high ownershipNice to HaveNextJS and TypeScript for frontend components and integration
AWS CDK for infrastructure‑as‑code and deployment
Broader AWS services knowledge: S3, CloudWatch, IAM, API Gateway
Experience with NoSQL databases
Experience integrating or serving custom models (inference, model endpoints, monitoring)
Familiarity with evaluation frameworks (offline tests, human feedback loops, regression suites)
Strong software engineering fundamentals: testing, CI/CD, observability, performanceWhat You Will DoPlan and develop applications that include custom AI components — orchestration, evaluation, tooling, and integrations
Build intelligent agents using LLMs and the MCP framework to enable context‑aware au
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