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

📌 AI Engineering Manager (Guadalajara)
🏢 Blend360
📍 Guadalajara