Senior Machine Learning Engineer
New
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ArionkoderAI consulting
Location: LATAMContractSenior
Salary not disclosed
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Job Details
- Languages
- Excellent written and spoken English
- Experience
- 5+ years of professional software engineering experience; 2+ years building LLM systems used by real users or delivered to clients.
- Required Skills
- AWSPythonCI/CD
Requirements
- 5+ years of professional software engineering experience with production ownership.
- 2+ years building LLM systems used by real users or delivered to clients.
- Advanced Python, including clean, tested, reviewable code; async; typing; packaging; and dependency management.
- Experience with structured extraction from unstructured documents.
- Hands-on RAG experience with chunking, embeddings, vector stores, reranking, and retrieval-quality measurement.
- Hands-on experience with agents, tool use, and MCP.
- Awareness of LLM security and privacy risks, including prompt injection, data leakage, and PII handling in client-data contexts.
- Experience designing and running evaluations, including evaluation datasets, graded criteria, baselines, and regression thresholds used as release gates.
- CI/CD and engineering hygiene, including Git, automated tests, reproducible builds, and containerization.
- Experience with an LLM orchestration framework such as LangChain/LangGraph, LlamaIndex, or Pydantic AI, and the ability to work without one.
- Cloud deployment of AI/ML workloads, preferably AWS or demonstrable equivalent on GCP or Azure.
- Practical cost and latency engineering for LLM systems, including token accounting, caching, model routing, and streaming.
- Daily hands-on use of AI coding tools such as Claude Code, Cursor, or Copilot, with judgment about when to trust their output.
- Ability to operate autonomously, challenge ill-posed problems, prioritize by risk and business impact, and own outcomes.
- Excellent written and spoken English; comfortable in a fully remote, distributed, client-facing team.
Responsibilities
- Build AI components for a governed data foundation used by AI agents, applications, and business teams.
- Develop systems for extraction and classification of complex legal documents, scanned documents, and digital documents.
- Build assistants and agents that solve ambiguous business problems.
- Write production-grade Python and ship code through CI/CD.
- Create curated evaluation datasets, graded criteria, baselines, and regression thresholds.
- Measure retrieval and model quality and use evaluations as release gates.
- Choose models and design solutions based on business constraints, cost, and latency.
- Demo solutions live and explain trade-offs to non-technical stakeholders.
- Size effort in pre-sales and turn project learnings into reusable company assets.
- Collaborate with the delivery team and client engineers who will run the systems.
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