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 ( ) 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

Qualifications :

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 GCPpreference for Azureplus 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

Additional Information :

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.

Remote Work :

Yes

Employment Type :

Full-time

Experience: years
Vacancy: 1