AI Engineering Lead

Architect and lead the implementation of production-grade AI solutions leveraging LLMs, transformer-based models, RAG pipelines, and agentic systems. Design, develop, and optimize multi-step AI agents capable of tool/API invocation, reasoning chains, and state management. Oversee the deployment of AI-powered applications across cloud environments (AWS, Azure, or GCP) ensuring scalability, reliability, and cost-efficiency. Establish best practices for prompt engineering, evaluation frameworks, and model performance monitoring (latency, grounding, factuality, cost). Implement guardrails and Responsible AI practices, including prompt injection mitigation, content moderation, bias reduction, and safety controls. Collaborate cross-functionally with Data Scientists, Software Engineers, and Product stakeholders to take AI solutions from prototype to full production ownership. Mentor and guide junior and mid-level engineers, ensuring high-quality, modular, and maintainable Python code standards. Drive experimentation strategies, define KPIs, and promote data-driven decision-making across AI initiatives. Degree in Computer Science, Data Science, Engineering, or related field (or equivalent practical experience). Strong expertise in transformer-based models and LLM architectures. Proven experience designing and deploying applied machine learning or generative AI systems to production environments. Hands-on experience with LLM frameworks such as LangChain, LlamaIndex, OpenAI API, CrewAI, Azure Prompt Flow, AWS Bedrock Agents, or similar tools. Advanced proficiency in Python and production-grade coding practices. Experience with containerization, CI/CD pipelines, versioning, and cloud-native architectures. Strong leadership skills with the ability to bridge rapid prototyping and scalable production deployment. Excellent collaboration and communication skills, with experience working at the intersection of Data Science and Engineering. 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. Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location, 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, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit We are seeking an AI Engineer Lead to contribute to our next level of growth and expansion. What is this position about? We are looking for our first AI Engineer Lead for our Guadalajara, Mexico office, to drive the design, architecture, and production deployment of advanced Generative AI solutions. This role will be responsible for leading the technical direction of LLM-based systems, agentic workflows, and applied AI initiatives, while collaborating closely with Engineering, Product, and ML Ops teams. The role requires strong hands-on expertise combined with technical leadership capabilities.