Lead AI Engineer

• Lead project delivery end to end, with governance, stakeholder communication, and accountability for outcomes
• Build and mentor a high-performing AI engineering team
• Establish technical standards and foster a culture of quality and pragmatism
• Own proposals and new business initiatives, defining technical feasibility and communicating risks and tradeoffs to clients
• Define realistic boundaries and expectations for AI systems
• Conduct technical reviews and architectural assessments
• Guide design and delivery of production-ready RAG systems, agentic frameworks, and LLM-powered solutions
• Lead advanced prompt engineering, including instruction design, few-shot sets, structured outputs, and tool/agent prompts
• Run feasibility assessments across prompting, RAG, fine-tuning, and classical ML
• Mentor engineers on AI system design and production deployment
• Design evaluation frameworks, including LLM-as-a-judge, recall@k, precision@k, and go/no-go gates
• Lead structured experiments across prompts, retrievers, chunking strategies, and models
• Establish practices for identifying and categorising model failures
• Set AI production reliability standards
• Build 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 for latency, cost, and reliability
• Lead infrastructure decisions balancing technical excellence and business efficiency

• 5+ years building and deploying AI solutions in production environments
• Expert Python proficiency
• Strong Git practices
• Experience with ML/LLM versioning and deployment
• Solid cloud experience across AWS, Azure, or GCP; preference for Azure
• Containerisation and orchestration knowledge
• Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation
• Proven MLOps/LLMOps track record using tools like MLflow, Weights and Biases, or similar
• Practical evaluation design skills: metrics, dataset curation, and structured experimentation
• Experience with event-driven architectures, APIs, and microservices
• Clear communication with engineering teams and senior stakeholders
• Strong hiring and team-building instincts with proven mentoring experience
• English: Advanced (required for effective communication with global teams and client leadership)
• 2+ years of direct team leadership or technical management responsibility

• Certifications in AWS, Databricks, and Snowflake
• Access to AI learning paths
• Study plans, courses, and additional certifications tailored to the role
• Access to Udemy Business
• English lessons
• Travel opportunities 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 working options
• Other benefits may vary according to location in LATAM