Job Description

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

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 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

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.
⚖️ Versátil 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.

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