AI Engineering Manager

Leadership and Delivery

• Lead project delivery end to end, with clear governance, stakeholder communication, and accountability for outcomes
• Build and mentor a high-performing AI engineering team, 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
• Define what AI systems should and should not attempt, setting realistic expectations and being upfront about limitations
• Conduct technical reviews and architectural assessments to maintain high standards across projects and team

AI Development

• Guide the design and delivery of RAG systems, agentic frameworks, and LLM-powered solutions that are robust enough for production
• Lead the application of advanced prompt engineering techniques including instruction design, few-shot sets, structured outputs, and tool/agent prompts
• Run feasibility assessments to choose the right approach for each problem: prompting, RAG, fine-tuning, or classical ML
• Mentor engineers on end-to-end AI system design and production deployment practices

Evaluation and Quality

• Design evaluation frameworks including LLM-as-a-judge approaches, metric creation (recall@k, precision@k), and go/no-go gates
• Lead structured experiments across prompts, retrievers, chunking strategies, and models, grounded in evidence not intuition
• Establish team practices for identifying and categorising model failures including hallucinations, retrieval misses, and instruction-following errors
• Set quality standards that ensure AI systems meet production reliability requirements

MLOps and Infrastructure

• Build scalable inference infrastructure and CI/CD pipelines for AI/ML models that support rapid iteration and reliable deployment
• Automate the full MLOps/LLMOps lifecycle: tracking, versioning, deployment, monitoring, and retraining across the team
• Design APIs, microservices, and orchestration layers optimised for latency, cost, and reliability
• Lead infrastructure decisions that balance technical excellence with business efficiency

What We Are Looking For

• 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, and experience with ML/LLM versioning and deployment
• Solid cloud experience across AWS, Azure, or GCP—preference for Azure—plus containerisation and 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, APIs, and microservices
• A clear communicator equally comfortable with engineering teams and senior stakeholders
• Strong hiring and team-building instincts with proven mentoring experience

What about languages?

• English: Advanced (required for effective communication with global teams and client leadership).

How much experience must I have?

7+ years of hands-on AI/ML engineering experience in production environments, with 2+ years of direct team leadership or technical management responsibility.

Nice to Have

• 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

Our Perks and Benefits

📚 Learning Opportunities

• Certifications in AWS (we are AWS Partners), Databricks, and Snowflake.
• Access to AI learning paths to stay up to date with the latest technologies.
• Study plans, courses, and additional certifications tailored to your role.
• Access to Udemy Business, offering thousands of courses to boost your technical and soft skills.
• English lessons to support your professional communication.

👨🏽‍💻 Travel opportunities to attend industry conferences and meet clients.

👩‍🏫 Mentoring and Development

• Career development plans and mentorship programs to help shape your path.

🎁 Celebrations & Support

• Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones.
• Company-provided equipment.

⚖️ Flexible working options to help you strike the right balance.

🏥 Statutory Benefits

• Social security coverage (IMSS).
• Christmas bonus (Aguinaldo) as per Mexican law.
• Vacation premium (Prima Vacacional).
• Remote work bonus.
• Paid leaves as per Federal Labor Law (LFT).
• Additional benefits as required by Mexican labor regulations.

Other benefits may vary. For detailed information, please consult with one of our recruiters.