AI Engineering Manager

This is a fully remote position, open to applicants in Chile.

📋 Description

• Oversee project delivery from start to finish, ensuring clear governance, effective communication with stakeholders, and accountability for results.

• Build and nurture a high-performing AI engineering team.

• Establish technical standards while promoting a culture of quality and practicality.

• Take ownership of proposals and new business initiatives, assessing technical feasibility and conveying risks and trade-offs to clients.

• Define suitable AI system capabilities and set realistic expectations regarding limitations.

• Conduct technical reviews and architectural evaluations.

• Guide the design and delivery of production-ready RAG systems, agentic frameworks, and LLM-driven solutions.

• Lead prompt engineering, feasibility studies, and comprehensive AI system design.

• Create evaluation frameworks, metrics, datasets, experiments, and quality gates.

• Establish practices for identifying and categorizing model failures.

• Set standards for the reliability of AI systems in production.

• Build scalable inference infrastructure and CI/CD pipelines.

• Automate the MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, and retraining.

• Design APIs, microservices, and orchestration layers optimized for latency, cost, and reliability.

• Make infrastructure decisions that balance technical excellence with business efficiency.

⛳️ Requirements

• A minimum of 7 years of experience in building and deploying AI solutions within production environments.

• At least 2 years of direct leadership or technical management experience.

• Proficiency in Python at an expert level.

• Strong practices in Git.

• Experience with ML/LLM versioning and deployment processes.

• Solid cloud experience across AWS, Azure, or GCP, with a preference for Azure.

• Knowledge of containerization and orchestration.

• Hands-on experience with RAG, including chunking, embeddings, retrieval, reranking, and evaluation.

• Proven experience in MLOps/LLMOps using tools such as MLflow, Weights and Biases, or similar.

• Practical skills in evaluation design, including metrics, dataset curation, and structured experimentation.

• Familiarity with event-driven architectures, APIs, and microservices.

• Strong communication skills with engineering teams and senior stakeholders.

• Demonstrated mentoring experience and a strong instinct for hiring and team building.

• Advanced English proficiency required for communication with global teams and client leadership.

🏝️ Benefits

• Certifications in AWS, Databricks, and Snowflake.

• Access to AI learning paths.

• Tailored study plans, courses, and additional certifications relevant to your role.

• Access to Udemy Business.

• English language lessons.

• Opportunities to travel for industry conferences and client meetings.

• Career development plans and mentorship programs.

• Special rewards for birthdays, work anniversaries, and other personal milestones.

• Company-provided equipment.

• Flexible working options.

• Additional benefits may vary based on your location in LATAM.