We are looking for a Backend Developer with hands-on production experience building real-world AI systems—including RAG pipelines, AI agents, and LLM integrations—running on AWS.

This role is for someone who lives in the code, not just in theory.

✅ What this means:

- Active Coder: Strong daily production experience writing services in Python and/or Java . 3yoe
- Production-Grade AI: Proven track record of building and deploying RAG pipelines, AI agents, or LLM-powered tools to live environments.
- Cloud AI Stack: Hands-on experience with cloud AI platforms ( AWS Bedrock preferred ; Azure OpenAI or GCP Vertex also accepted).
- Architecture & Depth: Able to explain how they built it—specific tools used, architectural decisions made, and technical tradeoffs.

❌ What this does NOT mean:
- A QA/SDET looking to "transition into AI"
- A Data Engineer who builds ETL or data pipelines but lacks experience with application services.
- An ML Researcher who trains models from scratch but has never shipped a production backend service.
- An Architect or Product Manager who hasn't written code in 2+ years.
- Someone whose only "AI experience" is using ChatGPT or Cursor as a coding assistant.

Mandatory Skills Description • AWS Bedrock — hands-on: model access, Knowledge Bases, Lambda integration (primary AI platform





• AI agents & Agentic tooling — practical knowledge of designing and operating AI agents, including agentic workflows, reusable skills, rules/guardrails, commands, and multi-tool/multi-agent orchestration

• RAG pipeline — end-to-end implementation: chunking, embedding, vector indexing, retrieval, generation

- Prompt engineering — zero-shot, few-shot, chain-of-thought, structured output (JSON mode), multi-turn
- Vector databases — working knowledge of OpenSearch, Pinecone, or Faiss; understands vector vs. graph DB difference

• LLM guardrails — input/output filtering, hallucination mitigation strategies

- Fine-tuning vs. RAG — ability to reason through which approach fits a given problem
- LLM orchestration — LangChain, LangGraph, or LlamaIndex
- Embeddings — understands semantic similarity; experience with Amazon Titan Embed or equivalent
- Python — for Lambda functions, AI pipeline scripting, and data processing

• Java — 3+ years of hands-on test automation development

- Appium / UiAutomator2 — mobile/Android UI automation
- Android / ADB — device management, test execution
- ReportPortal or equivalent test reporting tool
- REST API — concepts and hands-on usage
- Jenkins / CI-CD — pipeline debugging and integration
- AWS — S3, Lambda, API Gateway, IAM, OpenSearch Serverless
- Docker — containerized test execution environment

📌 Senior Backend AI Engineer (AWS, LLMs, RAG) (México)
🏢 Luxoft
📍 México