AI Engineer – Manager

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

📋 Description

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

• Create and develop production-ready RAG systems, agentic frameworks, and solutions powered by LLM.

• Utilize prompt engineering techniques, including instruction design, few-shot prompting, structured outputs, and tool/agent prompts.

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

• Establish AI evaluation frameworks utilizing LLM-as-a-judge, custom metrics, recall@k, precision@k, and go/no-go criteria.

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

• Detect and classify model failures, hallucinations, retrieval misses, instruction-following errors, and quality regressions.

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

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

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

• Provide mentorship to junior engineers.

• Contribute to proposals, solution design, and new business initiatives.

• Engage with engineering teams, senior stakeholders, and clients to provide guidance on practical AI decisions.

⛳️ Requirements

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

• Proficient in Python.

• Strong understanding of Git practices.

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

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

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

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

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

• Extensive experience with AWS, Azure, or GCP, with a preference for Azure.

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

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

• Excellent communication skills with engineering teams, senior stakeholders, and clients.

• A pragmatic approach that balances innovation, reliability, cost, latency, and business value.

• Advanced proficiency in English for both written and verbal communication.

• Nice to have: Experience with Databricks MLOps platform.

• Nice to have: Experience in LLM fine-tuning.

• Nice to have: Familiarity with agentic GenAI systems.

• Nice to have: Experience with Infrastructure as Code.

• Nice to have: Knowledge of security and observability practices for AI services.

• Nice to have: Background in classical machine learning.

• Nice to have: Contributions to open-source projects or public technical work.

🏝️ Benefits

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

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

• Flexible working arrangements.

• Provision of a Macbook and accessories.

• Daily availability of snacks and beverages at headquarters.

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

• Football league every Wednesday and Friday.

• Access to tennis courts.

• Chess championships and music/game nights.

• Opportunities for AWS certifications.

• Support for study plans, courses, and other certifications.

• English language lessons.

• Learning opportunities during Tech Tuesdays.

• Mentoring and development opportunities.

• Gifts for anniversaries and birthdays.