AI Engineering Manager
RemoteChile
Job Summary
Lead end-to-end project delivery with clear governance, stakeholder communication, and accountability for outcomes. Build and mentor a high-performing AI engineering team while establishing technical standards and fostering a culture of quality and pragmatism. Own proposals and new business initiatives, defining technical feasibility and communicating risks and tradeoffs clearly to clients. Guide the design and delivery of RAG systems, agentic frameworks, and LLM-powered solutions that are robust enough for production. Design evaluation frameworks including LLM-as-a-judge approaches and lead structured experiments across prompts and retrievers grounded in evidence. Build scalable inference infrastructure and automate the full MLOps/LLMOps lifecycle for tracking, versioning, and deployment. Conduct technical reviews and architectural assessments to maintain high standards across projects.
Required Qualifications
- 7+ years building and deploying AI solutions in production environments
- 2+ years of direct team leadership or technical management experience
- Expert Python proficiency
- Strong Git practices
- Experience with ML/LLM versioning and deployment
- Solid cloud experience across AWS, Azure, or GCP
- Containerisation knowledge
- Orchestration knowledge
- Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation
- Proven MLOps/LLMOps track record using tools like MLflow, Weights and Biases, or similar
- Practical evaluation design skills: metrics, dataset curation, and structured experimentation
- Experience with event-driven architectures
- Experience with APIs
- Experience with microservices
- A clear communicator equally comfortable with engineering teams and senior stakeholders
- Strong hiring and team-building instincts
- Proven mentoring experience
- English: Advanced
- 7+ years of hands-on AI/ML engineering experience in production environments
- 2+ years of direct team leadership or technical management responsibility
Desired Qualifications
- preference for Azure
- Databricks MLOps platform
- LLM fine-tuning experience
- Building agentic GenAI systems
- Infrastructure as Code
- Security and observability for AI services
- Classical ML background
- Open-source contributions
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