Machine Learning Engineer

🇧🇷 🇦🇷 🇨🇴 🇲🇽 EXCLUSIVE FOR CANDIDATES BASED IN BRAZIL, ARGENTINA, COLOMBIA, OR MEXICO 🇲🇽 🇨🇴 🇦🇷 🇧🇷

Requirements

Must-haves

• 2+ years of software engineering experience
• Experience with applied AI, or automation engineering experience
• Experience with Python and agentic frameworks (e.g., LangChain, LlamaIndex, AutoGen)
• Experience designing and deploying agents using tools (e.g., Crew AI, Agentuity, n8n)
• Proficiency with prompt engineering, memory strategies, and tool/function calling
• Ability to collaborate across departments to deliver AI solutions and promote adoption
• Ability to support business stakeholders in adapting workflows to agentic systems
• Experience managing delivery timelines and defining project success metrics
• Strong communication skills in both spoken and written English

Nice-to-haves

• Startup experience
• Bachelor's Degree in Computer Engineering, Computer Science, or equivalent

What you will work on

• Design, build, and manage LLM-based agents that automate internal operations across departments (e.g., Engineering, Product, Sales, HR, Field Operations)
• Identify high-impact use cases and deliver tailored agentic solutions from scoping to scaling
• Own the full lifecycle of AI-powered automations, ensuring seamless integration and long-term reliability
• Collaborate with product managers, engineers, and stakeholders to embed agents into real-world workflows
• Promote adoption by partnering with department leads to evolve processes and drive behavior change
• Design agentic systems using frameworks (e.g. Crew AI, Agentuity) to support complex workflows
• Integrate APIs and tools using automation platforms (e.g. n8n, Zapier, Make.com)
• Implement advanced techniques like prompt chaining, memory strategies, RAG, and tool-calling
• Monitor, maintain, and optimize deployed agents for performance, uptime, and value delivery
• Establish governance, testing, and issue resolution processes for production agents
• Lead agentic project plans, delivery timelines, and milestone tracking
• Document agent behavior, system logic, testing protocols, and maintenance procedures
• Report on solution performance, ROI, and progress to leadership and contribute to automation strategy