AI Engineer / GenAI Engineer

Role: AI Engineer / GenAI Engineer
Position Type: Full-Time Contract (40hrs/week)
Contract Duration: 6 months
Work Schedule: 8 hours/day (Mon-Fri)
Location: 100% Remote (Candidates can work from anywhere in Argentina, Brazil, Costa Rica, Chile, Colombia, Peru, Uruguay, Paraguay)

Client is looking for someone in line with the following:

An AI Engineer / GenAI Engineer with strong hands-on experience building and operating LLM-based solutions, agents, RAG pipelines, etc.

What You'd Be Doing
· Drive an AI-first culture through internal playbooks and "golden-path" templates while measuring impact via DORA and SPACE metrics.
· Manage AI costs through token budgeting and usage tracking alongside guardrails like PII redaction and audit logging.
· Build and document reusable patterns for code generation, PRs, testing, and debugging to optimize the end-to-end developer lifecycle.
· Conduct POCs and provide recommendations for AI tools based on ROI, technical merit, and stakeholder feedback.
· Manage lightweight AWS infrastructure including API Gateways and LLM pipelines while integrating tools with CI/CD and GitLab.

What We Need to See
· 8+ years in platform engineering, DevOps, developer experience, or a closely related technical discipline.
· Strong communication and stakeholder management skills
· Demonstrated hands-on experience with LLM APIs and AI developer tooling in production or organizational contexts.
· Experience evaluating, procuring, or governing AI/SaaS tools at an organizational level, including vendor assessment, license management, and cost governance.
· Strong Python skills for automation, tooling, and lightweight AI workflow and integration development.
· Practical, daily use of AI-assisted development tools (GitHub Copilot, Cursor, Claude Code, ChatGPT, or similar) in your own engineering workflows.
· Experience designing developer workflows, internal platforms, or engineering self-service capabilities with a focus on adoption and usability.
· Solid AWS experience with familiarity with Bedrock, API Gateway, or equivalent managed AI and cloud services.
· Strong observability mindset with the ability to instrument AI tooling and workflows with meaningful metrics and usage signals.
· Infrastructure-as-code familiarity (Terraform, Helm) and experience working within GitOps and CI/CD environments, with GitLab CI preferred.
· Excellent communication and stakeholder management skills, with the ability to translate technical findings into clear recommendations for engineering leadership and business audiences.

Ways to Stand Out from the Crowd
· Experience building or contributing to an internal AI enablement function, center of excellence, or developer experience program.
· Hands-on experience with LLM Agents, RAG pipelines, vector databases (pgvector, OpenSearch, Pinecone, or similar), and prompt orchestration frameworks such as LangChain or LlamaIndex.
· Familiarity with AI FinOps tooling, cost attribution models, and LLM API usage reporting at an organizational scale.
· Experience with AI governance frameworks including acceptable use policies, audit logging, PII redaction pipelines, and responsible AI practices in regulated enterprise environments.
· Background in financial services or insurance with an understanding of compliance constraints on AI tool usage and data handling.
· Experience with AI-specific security threat models including OWASP Top 10 for LLMs, prompt injection risks, and model supply chain security.
· Familiarity with developer productivity metrics frameworks such as DORA or SPACE, and a track record of using data to demonstrate engineering impact.
· Strong ownership demeanor with a structured, automation-first approach and demonstrated impact driving AI-first engineering practices across teams.