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.
Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Ble