Lead / Principal GenAI Engineer

DevsuGenerative AI

Colombia. Peru. ArgentinaContractLead

Salary not disclosed

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Job Details

Bring 8+ years of professional software development experience and a deep understanding of full-stack architectures.
Have hands-on experience shipping production AI/ML systems, including generative AI or agentic AI.
Demonstrate fluency with agentic tools such as Claude Code, Cursor, Codex, or GitHub Copilot and agentic architecture patterns.
Understand single- and multi-agent workflows, supervisor/worker patterns, state and memory management, orchestration, and safe action execution.
Bring strong knowledge of advanced RAG, embeddings, vector and hybrid search, re-ranking, tool or function calling, structured outputs, and context engineering.
Have strong Python skills and proficiency with LangChain, LangGraph, LlamaIndex, or comparable AI engineering frameworks.
Have working knowledge of cloud AI/ML platforms such as Bedrock, Azure AI Foundry, or Vertex AI, and enterprise data ecosystems such as Snowflake, Databricks, and vector databases.
Be able to set LLMOps/MLOps standards, including versioning, CI/CD, containerisation, and cost and performance optimisation.
Use sound judgment on architecture tradeoffs, model selection, retrieval strategy, latency, cost, security, and vendor lock-in, and explain tradeoffs to technical and non-technical stakeholders.
Have experience on Agile/Scrum teams and with large, complex systems.

Assess AI feasibility during diligence and set technical direction for data products, retrieval pipelines, model orchestration, agentic workflows, evaluation, deployment, and monitoring.
Design and build LLM workflows, retrieval systems, and agentic applications using portfolio companies’ enterprise data and infrastructure.
Define secure, scalable reference architectures for each portfolio company’s environment.
Establish evaluation and observability standards, including test sets, automated evaluations, tracing, quality scoring, latency and cost monitoring, and regression testing.
Build human-in-the-loop controls and apply sound data and security practices.
Develop prototypes that can evolve into production systems and transfer knowledge to portfolio company technical teams.
Evaluate emerging models, frameworks, and tools and recommend which are suitable for adoption.

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