Job Description We’re seeking a Lead AI Engineer with deep expertise in applied machine learning, LLMs, and agentic system design. You will be responsible for architecting and deploying production-grade AI systems — from Retrieval-Augmented Generation (RAG) pipelines to multi-step autonomous agents — while mentoring team members and driving engineering excellence. You’ll collaborate across Data Science, Product, and Engineering to bring ideas from prototype to scalable, cloud-native solutions. Key Responsibilities & Skills AI & Software Engineering Architect, build, and deploy end-to-end and scalable ML/AI systems including GenAI, RAG pipelines, and agentic workflows. Design and iterate on prompts, reasoning workflows, and tool integrations for LLM-powered automation. Integrate frameworks such as LangChain, LlamaIndex, CrewAI, or LangGraph to enable advanced multi-agent orchestration. Build evaluation frameworks to monitor performance, grounding, factuality, cost, and latency. Implement safety and governance: guardrails, output validation, and agent control mechanisms (e.g. tool call limits, loop prevention). Ensure alignment with traditional ML/AI methodologies while innovating with modern paradigms. MLOps & Software Engineering Apply MLOps best practices across experimentation, CI/CD, observability, and cloud deployment (AWS, Azure, GCP). Write clean, modular, production-grade code primarily in Python, and manage containerized deployments via Docker, GitHub Actions, etc. Develop scalable, reliable APIs and pipelines for batch and real-time inference. Leadership & Collaboration Act as a technical lead and project manager, ensuring timely and high-quality delivery. Mentor junior engineers and data scientists to grow technical capability across the team. Collaborate with cross-functional stakeholders to translate complex technical solutions into business value. Communication Skills Translate technical outcomes into clear business impact for both internal and external stakeholders. Excellent English communication skills (written and verbal) are essential for working with global teams and clients. Qualifications Must-Have 5+ years in AI/ML engineering, with hands-on experience in NLP, transformers, or LLMs. Proven experience building RAG systems, agentic applications, or GenAI-powered tools. Strong Python skills and familiarity with AI orchestration libraries (e.g., LangChain, CrewAI, LangGraph). Experience deploying ML/LLM models to production (API, batch, streaming), with monitoring and testing frameworks in place. Working knowledge of cloud services (AWS, Azure, GCP) and CI/CD best practices. Nice-to-Have Experience building RAG systems and agentic applications. Knowledge of or experience with Snowflake and Snowpark. Background in LLM evaluation and prompt engineering. Additional Information Our Perks and Benefits: Learning Opportunities: Certifications in AWS (we are AWS Partners), Databricks, and Snowflake. Access to AI learning paths to stay up to date with the latest technologies. Study plans, courses, and additional certifications tailored to your role. Access to Udemy Business, offering thousands of courses to boost your technical and soft skills. English lessons to support your professional communication. Mentoring and Development: Career development plans and mentorship programs to help shape your path. Celebrations & Support: Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones. Company-provided equipment. ⚖️ Flexible working options to help you strike the right balance. 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.