Forward Deployed AI Engineer (Python) – Remote – Latin America

About the role

Forward Deployed AI Engineers embed within client teams to identify genuine AI opportunities, design end‑to‑end solutions, and ensure they survive production. The role blends consulting, operations, and engineering: you’ll pressure‑test business cases with senior leaders and troubleshoot model behavior after data changes. Success means turning discovery conversations on Monday into shipped, production‑ready agents by Thursday.

Key responsibilities

• Run workshops with business, data, and engineering stakeholders to discover AI‑fit problems and assess data readiness.
• Architect and design agentic systems, focusing on retrieval pipelines, embeddings, hybrid search, knowledge graphs, and human‑in‑the‑loop guardrails.
• Build and ship production‑grade agents, retrieval services, and evaluation tooling, delivering prototypes in weeks.
• Own CI/CD for AI components, including prompt/versioning, evaluation suites, and release gating.
• Operate post‑launch systems: monitor grounding, latency, cost, drift, and handoff to client teams.
• Present technical decisions to CTOs, VPs of Engineering, CDOs, and support pre‑sales scoping when needed.

Required profile

• 8–10+ years of experience across machine learning, data, and software engineering.
• Python‑first production experience (typed, tested, deployed services) with exposure to TypeScript/Node.NET/C#, or Java.
• Proven track record as an ML/Data Architect, Principal Engineer, or Tech Lead, including CI/CD ownership for AI/ML systems.
• Hands‑on experience deploying LLM‑based agents in production and managing retrieval, embeddings, and model routing.
• Consultative discovery skills: ability to uncover business drivers, stakeholder constraints, and align goals.

Required skills

• Python
• TypeScript / Node.js
• .NET / C#
• Java
• CI/CD for AI (LLMOps / MLOps)
• Embeddings, vector and hybrid search, reranking
• Knowledge graphs
• Agentic tooling (Claude, tool/function calling, multi‑agent orchestration)
• Data integration: Snowflake, Databricks, BigQuery, APIs, event streams, iPaaS