Job Description

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

Qualifications

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 general 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.

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.

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