AI Engineer Manager

TL;DR

AI Engineer Manager (RAG/LLMOps): Designing, building, and deploying production-ready AI systems for client projects with an accent on RAG pipelines, agentic frameworks, LLM-powered solutions, and evaluation design. Focus on leading end-to-end delivery, building scalable inference and MLOps/LLMOps infrastructure, and balancing reliability, latency, cost, and business value.

Location: Remote within LATAM

Company

hirify.global is an AI services provider helping clients create business impact through data science, artificial intelligence, technology, and human expertise.

What you will do

• Lead AI project delivery from feasibility assessment through production deployment, governance, and continuous improvement.
• Design and build robust RAG systems, agentic frameworks, and LLM-powered applications.
• Develop evaluation frameworks using custom metrics, LLM-as-a-judge, recall@k, precision@k, datasets, and quality gates.
• Run structured experiments across prompts, retrievers, chunking, embeddings, reranking approaches, and models.
• Build scalable inference infrastructure, APIs, microservices, CI/CD pipelines, and orchestration layers.
• Automate MLOps/LLMOps workflows while mentoring engineers and supporting solution design, proposals, and client initiatives.

Requirements

• At least 6 years of professional experience building and deploying AI, ML, or software solutions in production.
• Strong Python and Git skills with hands-on experience delivering LLM-powered solutions and RAG systems.
• Experience with prompt engineering, structured outputs, few-shot prompting, tool and agent prompts, and AI evaluation strategies.
• Experience with MLOps/LLMOps tools such as MLflow or Weights & Biases, plus cloud experience with AWS, Azure, or GCP.
• Experience with containerization, orchestration, scalable inference, APIs, microservices, event-driven architectures, and production engineering.
• Advanced English for written and verbal communication, with the ability to work with engineers, senior stakeholders, and clients.

Nice to have

• Databricks MLOps, LLM fine-tuning, agentic GenAI systems, or Infrastructure as Code experience.
• Knowledge of security and observability practices for AI services.
• Classical machine learning experience or open-source and public technical contributions.

Culture & Benefits

• Flexible working options with equipment provided, including a MacBook and accessories.
• Learning support through AWS certifications, study plans, courses, certifications, English lessons, and Tech Tuesdays.
• Mentoring and development opportunities to support career growth.
• Headquarters perks include daily lunches, snacks, beverages, sports activities, games, music nights, and team events.
• Anniversary and birthday gifts.

Hiring process

• Submit a resume or LinkedIn profile for consideration.
• The hiring team will review the application and explore potential collaboration.