AI Engineering Manager

Blend360; Región Centro; Presencial; MXN 900.000 - 1.500.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

AWS Certifications

Databricks Certification

Snowflake Certification

AI learning paths

Udemy Business

English lessons

Remote work bonus

IMSS social security

Descripción de la vacante

Blend360 seeks an AI Engineering Manager to lead end-to-end AI delivery, mentor a high-performing engineering team, and shape technical strategy across RAG systems, LLM-powered solutions, and production-grade MLOps. The role emphasizes clear client communication, architectural governance, and scalable infrastructure in a fast-growing, data-driven services environment.

Ideal candidates bring 7+ years in AI/ML with leadership experience, deep Python expertise, cloud proficiency, and a proven track

Formación

• 7+ years building and deploying AI solutions in production environments.
• 2+ years of direct team leadership or technical management experience.
• Expert Python proficiency, strong Git practices, and experience with ML/LLM versioning and deployment.
• Solid cloud experience across AWS, Azure, or GCP—preference for Azure—plus containerisation and orchestration knowledge.
• Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation.
• Proven MLOps/LLMOps track record using tools like MLflow, Weights and Biases, or similar.
• Practical evaluation design skills: metrics, dataset curation, and structured experimentation.
• Experience with event-driven architectures, APIs, and microservices.
• A clear communicator equally comfortable with engineering teams and senior stakeholders.
• Strong hiring and team-building instincts with proven mentoring experience.

Responsabilidades

• Lead project delivery end to end, with 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
• 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
• 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
• 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

Conocimientos

Python

Git

ML/LLM deployment

Cloud platforms (AWS/Azure/GCP)

Containerization / Kubernetes

RAG / retrieval augmentation

MLOps / LLMOps

Experimentation / evaluation

APIs / microservices

Clear communication with stakeholders

Herramientas

MLflow

Weights & Biases

Databricks

Snowflake

Descripción del empleo

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 www.blend360.com

We are seeking an AI Engineering Manager to contribute to our next level of growth and expansion.

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

Qualifications

What We Are Looking For

• 7+ years building and deploying AI solutions in production environments
• 2+ years of direct team leadership or technical management experience
• Expert Python proficiency, strong Git practices, and experience with ML/LLM versioning and deployment
• Solid cloud experience across AWS, Azure, or GCP—preference for Azure—plus containerisation and orchestration knowledge
• Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation
• Proven MLOps/LLMOps track record using tools like MLflow, Weights and Biases, or similar
• Practical evaluation design skills: metrics, dataset curation, and structured experimentation
• Experience with event-driven architectures, APIs, and microservices
• A clear communicator equally comfortable with engineering teams and senior stakeholders
• Strong hiring and team-building instincts with proven mentoring experience

What about languages?

• English: Advanced (required for effective communication with global 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 Have

• Databricks MLOps platform
• Building agentic GenAI systems
• Infrastructure as Code
• Security and observability for AI services
• Classical ML background
• Open-source contributions

Additional Information

Our 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.