AI Engineering Lead

What is this position about?

• Lead end-to-end project delivery with clear governance and strong stakeholder communication
• Mentor junior engineers and contribute to proposals and new business initiatives
• Define what AI systems should and should not attempt, and communicate risks and tradeoffs transparently to clients
• Design and build RAG systems, agentic frameworks, and LLM-powered solutions robust enough for production
• Apply advanced prompt engineering techniques, including instruction design, few-shot sets, structured outputs, and tool/agent prompts
• Lead feasibility assessments to select the right approach among prompting, RAG, fine-tuning, or classical ML
• Design evaluation frameworks, including LLM-as-a-judge methods, custom metrics (recall@k, precision@k), and go/no-go gates
• Run structured experiments across prompts, retrievers, chunking strategies, and models, grounded in evidence rather than intuition
• Identify and categorize model failure modes, including hallucinations, retrieval misses, and instruction-following errors
• Build scalable inference infrastructure and CI/CD pipelines for AI/ML models
• Automate the full MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, and retraining
• Design APIs, microservices, and orchestration layers optimized for latency, cost, and reliability
• Expert-level Python, strong Git practices, and experience with ML/LLM versioning
• Solid cloud experience across AWS, Azure, or GCP (Azure preferred), plus containerization and orchestration
• Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation
• Proven MLOps/LLMOps track record using tools such as MLflow, Weights & Biases, or similar
• Practical evaluation design skills, including metrics, dataset curation, and structured experimentation
• Experience with event-driven architectures, APIs, and microservices
• Strong communication skills, equally comfortable engaging engineering teams and senior stakeholders
• Preferred: experience with the Databricks MLOps platform, LLM fine-tuning, building agentic GenAI systems, Infrastructure as Code, security and observability for AI services, a classical ML background, and open-source contributions

• What about languages?
• English: Advanced (required for effective communication with global teams)
• How much experience must I have?
• 6+ years of experience building and deploying AI solutions in production environments, with a strong track record across RAG, agentic systems, and MLOps/LLMOps.

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.