AI ENGINEERING LEAD

<p><strong>What is this position about?</strong></p><ul><li>Lead end-to-end project delivery with clear governance and strong stakeholder communication</li><li>Mentor junior engineers and contribute to proposals and new business initiatives</li><li>Define what AI systems should and should not attempt, and communicate risks and tradeoffs transparently to clients</li><li>Design and build RAG systems, agentic frameworks, and LLM-powered solutions robust enough for production</li><li>Apply advanced prompt engineering techniques, including instruction design, few-shot sets, structured outputs, and tool/agent prompts</li><li>Lead feasibility assessments to select the right approach among prompting, RAG, fine-tuning, or classical ML</li><li>Design evaluation frameworks, including LLM-as-a-judge methods, custom metrics (recall@k, precision@k), and go/no-go gates</li><li>Run structured experiments across prompts, retrievers, chunking strategies, and models, grounded in evidence rather than intuition</li><li>Identify and categorize model failure modes, including hallucinations, retrieval misses, and instruction-following errors</li><li>Build scalable inference infrastructure and CI/CD pipelines for AI/ML models</li><li>Automate the full MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, and retraining</li><li>Design APIs, microservices, and orchestration layers optimized for latency, cost, and reliability</li></ul> <ul><li>Expert-level Python, strong Git practices, and experience with ML/LLM versioning</li><li>Solid cloud experience across AWS, Azure, or GCP (Azure preferred), plus containerization and orchestration</li><li>Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation</li><li>Proven MLOps/LLMOps track record using tools such as MLflow, Weights &amp; Biases, or similar</li><li>Practical evaluation design skills, including metrics, dataset curation, and structured experimentation</li><li>Experience with event-driven architectures, APIs, and microservices</li><li>Strong communication skills, equally comfortable engaging engineering teams and senior stakeholders</li><li>Preferred: experience with the Databricks MLOps platform, LLM fine-tuning, building agentic GenAI systems, Infrastructure as Code, security and observability for AI services, a classical ML background, and open-source contributions</li></ul><p><strong>What about languages?</strong></p><p>English: Advanced (required for effective communication with global teams)</p><p><strong>How much experience must I have?</strong></p><p>6+ years of experience building and deploying AI solutions in production environments, with a strong track record across RAG, agentic systems, and MLOps/LLMOps.</p> <p><strong>Our perks and benefits:</strong></p><p>📚 Learning Opportunities:</p><ul><li>Certifications in AWS (we are AWS Partners), Databricks, and Snowflake.</li><li>Access to AI learning paths to stay up to date with the latest technologies.</li><li>Study plans, courses, and additional certifications tailored to your role.</li><li>Access to Udemy Business, offering thousands of courses to boost your technical and soft skills.</li><li>English lessons to support your professional communication.</li></ul><p>👨🏽‍💻 Travel opportunities to attend industry conferences and meet clients.</p><p>👩‍🏫 Mentoring and Development:</p><ul><li>Career development plans and mentorship programs to help shape your path.</li></ul><p>🎁 Celebrations &amp; Support:</p><ul><li>Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones.</li><li>Company-provided equipment.</li></ul><p>⚖️ Flexible working options to help you strike the right balance.</p><p>Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location, please consult with one of our recruiters.</p>