AI Engineer Manager

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

📋 Description

• Oversee the complete delivery of AI projects, ensuring effective governance, stakeholder communication, and dependable execution.

• Develop and implement production-ready RAG systems, agentic frameworks, and solutions powered by LLM.

• Utilize prompt engineering strategies, such as instruction design, few-shot prompting, structured outputs, and tool/agent prompts.

• Conduct feasibility assessments across prompting, RAG, fine-tuning, classical ML, and hybrid solutions.

• Create AI evaluation frameworks leveraging LLM-as-a-judge, custom metrics, recall@k, precision@k, and go/no-go decision gates.

• Execute structured experiments involving prompts, retrievers, chunking, embeddings, reranking, and models.

• Recognize and classify model failures and quality regressions.

• Construct scalable inference infrastructure and CI/CD pipelines for AI and ML models.

• Automate the MLOps/LLMOps lifecycle encompassing tracking, versioning, deployment, monitoring, retraining, and continuous enhancement.

• Develop APIs, microservices, and orchestration layers that are optimized for latency, cost, reliability, and scalability.

• Provide mentorship to junior engineers.

• Engage in proposals, solution design, and new business initiatives.

• Maintain communication with engineering teams, senior stakeholders, and clients.

⛳️ Requirements

• Minimum of 6 years of professional experience in building and deploying AI, ML, data-driven, or software solutions in production settings.

• Strong proficiency in Python.

• Solid practices in Git.

• Practical experience with LLM-powered solutions, RAG systems, and contemporary GenAI development methodologies.

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

• Deep understanding of prompt engineering, structured outputs, few-shot prompting, instruction design, and tool/agent prompts.

• Experience in designing AI evaluation strategies, including metrics, dataset curation, structured experimentation, and quality gates.

• Familiarity with MLOps/LLMOps tools such as MLflow, Weights & Biases, or similar platforms.

• Robust cloud experience with AWS, Azure, or GCP.

• Experience with containerization, orchestration, scalable inference, APIs, and microservices.

• Knowledge of event-driven architectures and production-grade engineering practices.

• Ability to communicate effectively with engineering teams, senior stakeholders, and clients.

• Practical mindset that balances innovation, reliability, cost, latency, and business value.

• Advanced English skills required for both written and verbal communication.

• Azure experience is preferred.

• Additional experience in Databricks MLOps, LLM fine-tuning, agentic GenAI systems, Infrastructure as Code, security and observability practices, classical machine learning, or open-source/public technical work is a plus.

🏝️ Benefits

• Flexible working options.

• Provision of all necessary equipment, including a Macbook and accessories.

• Daily lunches at headquarters, offering vegetarian, vegan, gluten-free, and sugar-free choices.

• Gourmet meals every Friday prepared by an on-site chef at headquarters.

• Availability of snacks and beverages daily at headquarters.

• After-office events, including football, tennis, and game nights at headquarters.

• Participation in a football league.

• Access to tennis courts for friendly matches.

• Hosting of chess championships, game nights, and music nights.

• Opportunities for AWS certifications, study plans, courses, and other certifications.

• English language lessons.

• Learning opportunities every Tech Tuesday.

• Mentoring and professional development opportunities.

• Gifts for anniversaries and birthdays.