Lead AI Engineer

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

📋 Description

• Oversee the entire project delivery process, ensuring governance, effective communication with stakeholders, and accountability for results.

• Develop and guide a high-performing AI engineering team through mentorship.

• Set technical standards and promote a culture that values quality and practicality.

• Take ownership of proposals and new business initiatives, assessing technical feasibility and conveying risks and trade-offs to clients.

• Define achievable boundaries and expectations for AI systems.

• Perform technical reviews and architectural evaluations.

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

• Lead sophisticated prompt engineering efforts, including instruction design, few-shot sets, structured outputs, and tool/agent prompts.

• Conduct feasibility assessments related to prompting, RAG, fine-tuning, and traditional ML.

• Mentor engineers in AI system design and deployment in production.

• Create evaluation frameworks, such as LLM-as-a-judge, recall@k, precision@k, and go/no-go gates.

• Conduct structured experiments involving prompts, retrievers, chunking strategies, and models.

• Establish methodologies for identifying and classifying model failures.

• Set standards for AI production reliability.

• 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 with a focus on latency, cost, and reliability.

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

⛳️ Requirements

• Over 5 years of experience in building and deploying AI solutions in production settings.

• Proficient in Python at an expert level.

• Strong understanding of Git practices.

• Experience with ML/LLM versioning and deployment processes.

• Solid background in cloud technologies, particularly AWS, Azure, or GCP, with a preference for Azure.

• Knowledge of containerization and orchestration.

• Practical experience with RAG, including chunking, embeddings, retrieval, reranking, and evaluation.

• Proven track record in MLOps/LLMOps using tools such as MLflow, Weights and Biases, or similar.

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

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

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

• Strong instincts for hiring and team-building, backed by mentoring experience.

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

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

🏝️ Benefits

• Certifications in AWS, Databricks, and Snowflake.

• Access to AI learning paths.

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

• Access to Udemy Business.

• English language lessons.

• Opportunities to travel for industry conferences and client meetings.

• Career development plans and mentorship programs.

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

• Company-provided equipment.

• Flexible working options.

• Additional benefits may vary based on location in LATAM.