Remote AI Engineer Manager — RAG, LLMs, Production Systems

Blend; Región Centro; Presencial; MXN 1.029.000 - 1.887.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Everyday lunches

Flexible working options

Macbook and equipment

Snacks and beverages

Mentoring and development

Team building & events

Descripción de la vacante

Blend, a premier AI services provider, seeks an AI Engineer Manager to lead LATAM remote efforts, architecting production-ready AI systems spanning RAG, agentic frameworks, and LLM-powered solutions. You will drive feasibility, evaluation design, and scalable inference while mentoring junior engineers and shaping client solutions.

Ideal candidates have 6+ years building AI/ML in production, strong Python and Git skills, extensive GenAI/MLOps experience, and cloud exposure (AWS/Azure/GCP).

Formación

• 6+ years of experience building and deploying AI, ML, or data-driven solutions in production environments.
• Strong expertise in Python and solid Git practices.
• Hands-on experience with LLM-powered solutions, RAG systems, and modern GenAI development patterns.
• Practical experience with RAG components, including chunking, embeddings, retrieval, reranking, and evaluation.
• Strong understanding of prompt engineering techniques, including structured outputs, few-shot prompting, instruction design, and tool/agent prompts.
• Proven experience designing evaluation strategies for AI systems, including metrics, dataset curation, structured experimentation, and quality gates.
• Experience with MLOps/LLMOps practices and tools such as MLflow, Weights & Biases, or similar platforms.
• Solid cloud experience with AWS, Azure, or GCP. Azure experience is preferred.
• Experience with containerization, orchestration, scalable inference, APIs, and microservices.
• Understanding of event-driven architectures and production-grade engineering practices.
• Ability to communicate clearly with engineering teams, senior stakeholders, and clients.
• A pragmatic approach to AI delivery, balancing innovation, reliability, cost, latency, and business value.

Responsabilidades

• Leading AI project delivery end to end, ensuring clear governance, strong stakeholder communication, and reliable execution.
• Designing and building robust RAG systems, agentic frameworks, and LLM-powered solutions suitable for production environments.
• Applying advanced prompt engineering techniques, including instruction design, few-shot prompting, structured outputs, and tool/agent prompts.
• Leading feasibility assessments to determine the right technical approach, including prompting, RAG, fine-tuning, classical ML, or hybrid solutions.
• Designing evaluation frameworks for AI systems, including LLM-as-a-judge, custom metrics, recall@k, precision@k, and go/no-go gates.
• Running structured experiments across prompts, retrievers, chunking strategies, embeddings, reranking approaches, and models.
• Identifying and categorizing model failures such as hallucinations, retrieval misses, instruction-following errors, and quality regressions.
• Building scalable inference infrastructure and CI/CD pipelines for AI and ML models.
• Automating the MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, retraining, and continuous improvement.
• Designing APIs, microservices, and orchestration layers optimized for latency, cost, reliability, and scalability.
• Mentoring junior engineers and contributing to proposals, solution design, and new business initiatives.

Conocimientos

Python

GenAI

LLM/GenAI

MLOps

AWS

Azure

GCP

Herramientas

MLflow

Weights&Biases

Databricks

Docker

Kubernetes

Descripción del empleo

Blend, a premier AI services provider, seeks an AI Engineer Manager to lead LATAM remote efforts, architecting production-ready AI systems spanning RAG, agentic frameworks, and LLM-powered solutions. You will drive feasibility, evaluation design, and scalable inference while mentoring junior engineers and shaping client solutions.

Ideal candidates have 6+ years building AI/ML in production, strong Python and Git skills, extensive GenAI/MLOps experience, and cloud exposure (AWS/Azure/GCP).