AI Engineering Manager

This is a fully remote position, open to applicants in Argentina.

📋 Description

• Oversee the complete project delivery process, ensuring governance, effective stakeholder communication, and accountability for results.

• Develop and nurture a high-performing AI engineering team while establishing technical standards.

• Manage proposals and new business opportunities, defining technical feasibility and articulating associated risks and trade-offs.

• Specify realistic capabilities and limitations of AI systems.

• Perform technical reviews and conduct architectural assessments.

• Direct the design and delivery of production-ready RAG systems, agentic frameworks, and LLM-powered solutions.

• Lead prompt engineering efforts, focusing on instruction design, few-shot sets, structured outputs, and tool or agent prompts.

• Evaluate feasibility and choose between prompting, RAG, fine-tuning, or classical ML methodologies.

• Provide mentorship to engineers on AI system design and production deployment.

• Create evaluation frameworks, metrics, datasets, experiments, and go/no-go criteria.

• Establish practices for identifying and categorizing model failures.

• Set standards for production reliability in AI systems.

• Develop scalable inference infrastructure and CI/CD pipelines.

• Automate the MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, and retraining.

• Design APIs, microservices, and orchestration layers optimized for latency, cost, and reliability.

• Make infrastructure decisions that balance technical excellence with business efficiency.

⛳️ Requirements

• A minimum of 7 years of experience in building and deploying AI solutions in production settings.

• At least 2 years of direct experience in team leadership or technical management.

• Proficiency in Python at an expert level.

• Strong practices in Git.

• Experience with ML/LLM versioning and deployment processes.

• Solid background in cloud services, specifically AWS, Azure, or GCP, with a preference for Azure.

• Knowledge of containerization and orchestration techniques.

• Hands-on experience with RAG techniques including chunking, embeddings, retrieval, reranking, and evaluation.

• Demonstrated experience in MLOps/LLMOps using tools such as MLflow, Weights and Biases, or similar platforms.

• Skills in practical evaluation design, including metrics, dataset curation, and structured experimentation.

• Familiarity with event-driven architectures, APIs, and microservices.

• Ability to communicate effectively with engineering teams and senior stakeholders.

• Strong instincts for hiring and team-building.

• Proven track record in mentoring others.

• Advanced English proficiency required for effective communication with global teams and client leadership.

🏝️ Benefits

• Certifications in AWS, Databricks, and Snowflake.

• Access to AI learning paths.

• Tailored study plans, courses, and additional certifications relevant to the role.

• Access to Udemy Business.

• English language lessons.

• Opportunities for travel to attend industry conferences and meet clients.

• Career development plans and mentorship programs.

• Special day rewards for birthdays, work anniversaries, and other personal milestones.

• Company-provided equipment.

• Flexible work options.

• Additional benefits may vary based on location in LATAM.