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 are seeking an AI Engineering Manager to contribute to our next level of growth and expansion.Job DescriptionLeadership and DeliveryLead project delivery end to end, with clear governance, stakeholder communication, and accountability for outcomesBuild and mentor a high-performing AI engineering team, establishing technical standards and fostering a culture of quality and pragmatismOwn proposals and new business initiatives, defining technical feasibility and communicating risks and tradeoffs clearly to clientsDefine what AI systems should and should not attempt, setting realistic expectations and being upfront about limitationsConduct technical reviews and architectural assessments to maintain high standards across projects and teamAI DevelopmentGuide the design and delivery of RAG systems, agentic frameworks, and LLM-powered solutions that are robust enough for productionLead the application of advanced prompt engineering techniques including instruction design, few-shot sets, structured outputs, and tool/agent promptsRun feasibility assessments to choose the right approach for each problem: prompting, RAG, fine-tuning,



or classical MLMentor engineers on end-to-end AI system design and production deployment practicesEvaluation and QualityDesign evaluation frameworks including LLM-as-a-judge approaches, metric creation (recall@k, precision@k), and go/no-go gatesLead structured experiments across prompts, retrievers, chunking strategies, and models, grounded in evidence not intuitionEstablish team practices for identifying and categorising model failures including hallucinations, retrieval misses, and instruction-following errorsSet quality standards that ensure AI systems meet production reliability requirementsMLOps and InfrastructureBuild scalable inference infrastructure and CI/CD pipelines for AI/ML models that support rapid iteration and reliable deploymentAutomate the full MLOps/LLMOps lifecycle: tracking, versioning, deployment, monitoring, and retraining across the teamDesign APIs, microservices, and orchestration layers optimised for latency, cost, and reliabilityLead infrastructure decisions that balance technical excellence with business efficiencyQualificationsWhat We Are Looking For7+ years building and deploying AI solutions in production environments2+ years of direct team leadership or technical management experienceExpert Python proficiency, strong Git practices, and experience with ML/LLM versioning and deploymentSolid cloud experience across AWS, Azure, or GCP—preference for Azure—plus containerisation and orchestration knowledgeHands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluationProven MLOps/LLMOps track record using tools like MLflow, Weights and Biases,



or similarPractical evaluation design skills: metrics, dataset curation, and structured experimentationExperience with event-driven architectures, APIs, and microservicesA clear communicator equally comfortable with engineering teams and senior stakeholdersStrong hiring and team-building instincts with proven mentoring experienceWhat about languages?
English: Advanced (required for effective communication with general teams and client leadership).
How much experience must I have?
7+ years of hands-on AI/ML engineering experience in production environments, with 2+ years of direct team leadership or technical management responsibility.Nice to HaveDatabricks MLOps platformBuilding agentic GenAI systemsInfrastructure as CodeSecurity and observability for AI servicesClassical ML backgroundOpen-source contributionsAdditional InformationOur Perks and Benefits: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.Social security coverage (IMSS).
Remote work bonus.Paid leaves as per Federal Labor Law (LFT).
Additional benefits as required by Mexican labor regulations.
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📌 Ai Engineering Manager (Jalisco)
🏢 Blend360
📍 Jalisco