Remote AI Engineering Manager — Lead Production AI Systems

Blend; Bogotá; Presencial; COP 180.000.000 - 280.000.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Everyday lunches at HQ

Flexible working options

MacBook and accessories

Descripción de la vacante

Blend is seeking an AI Engineer Manager to join our LATAM team in a remote, full-time role. You will design, build, and deploy production-ready AI systems, including RAG pipelines and LLM-powered solutions, while mentoring engineers and guiding client-facing decisions.

You will lead end-to-end AI project delivery, implement evaluation frameworks, and drive MLOps/LLMOps practices across cloud and on-prem environments. Strong communication with stakeholders is essential.

Formación

• 6+ years of production AI/ML experience.
• Strong Python and Git skills with production workflows.
• Hands-on with LLM-powered solutions, RAG, and GenAI patterns.
• Experience with MLOps/LLMOps (MLflow, W&B) and cloud platforms.
• Excellent communication with engineers, stakeholders, and clients.

Responsabilidades

• Lead AI project delivery end-to-end with governance and stakeholder updates.
• Design robust RAG systems, agentic frameworks, and production-ready LLM solutions.
• Apply advanced prompt engineering, evaluation metrics, and testing.
• Assess feasibility and choose prompts, RAG, fine-tuning, or hybrid approaches.
• Build scalable inference, CI/CD pipelines, and orchestration layers.
• Mentor engineers and contribute to proposals and new business opportunities.

Conocimientos

Python

Git

LLM development

RAG systems

Prompt engineering

MLOps/LLMOps

Stakeholder communication

Herramientas

MLflow

Weights & Biases

Databricks

Cloud platforms (AWS/Azure/GCP)

Descripción del empleo

Blend is seeking an AI Engineer Manager to join our LATAM team in a remote, full-time role. You will design, build, and deploy production-ready AI systems, including RAG pipelines and LLM-powered solutions, while mentoring engineers and guiding client-facing decisions.

You will lead end-to-end AI project delivery, implement evaluation frameworks, and drive MLOps/LLMOps practices across cloud and on-prem environments. Strong communication with stakeholders is essential.