AI Automation Engineer

Open to candidates based in the US, Canada, and LATAM. Role Overview We are hiring a senior independent contractor to design and ship production-grade AI agents that automate real business processes. This is a hands-on building role for someone who lives at the intersection of business-process engineering and applied AI: you will map how work flows today, redesign it around automation, and then build the agents and orchestration that make the new process run. You will own builds end to end — from process discovery through to agents deployed on Azure (Container Apps and Function Apps) and integrated into the company's broader systems and orchestration layer. You are equally comfortable reasoning about a messy operational workflow and writing the code that automates it, using modern agent-building tooling such as Codex and Claude Code. Engagement at a Glance Engagement type: Independent contractor (1099) , full-time Hours: 40 hours per week Duration: 3-month term; extension likely based on performance Location: Remote (U.S. business-hours overlap required) Initial 3-month term; extension likely based on performance Remote (U.S. business-hours overlap required) Reports to: AI Practice Lead Start: As soon as possible What You'll Do Connect business process to automation. Translate operational workflows into AI- automation opportunities and identify where agents create the most leverage. Process map, then reengineer. Build process maps of current-state workflows and use them to redesign and reengineer processes for automation — not just digitizing the old way of working. Build agents from the ground up. Architect and code agents from scratch using tools such as Codex and Claude Code, with clean, maintainable, testable implementations. Ship agentic builds on Azure. Deploy and run agents in Azure Container Apps and Azure Function Apps, including packaging, configuration, secrets, scaling, and monitoring. Design for the larger ecosystem. Build agents that fit the company's broader systems and orchestration layer — multi-agent coordination, tool and data integrations, state, and handoffs across the ecosystem. Optimize cost and performance. Make deliberate model-selection and cost-optimization decisions, balancing capability, latency, and spend across model tiers and routing strategies. Make it production-ready. Add evaluation, observability, error handling, and guardrails so builds are reliable, measurable, and safe to run against real work. What You Bring (Required) Demonstrated experience building AI agents from scratch — not just prompt-tuning or wiring no-code tools — using modern coding agents (e.g., Codex, Claude Code) and agent frameworks. Strong command of agent orchestration: multi-agent patterns, tool use, retrieval, memory and state, and coordinating agents within a larger system. Hands-on Azure experience deploying services to Azure Container Apps and/or Azure Function Apps (containers, triggers, scaling, configuration, secrets). Proven ability to process-map a workflow and reengineer it for automation, working directly with business stakeholders to redesign how work gets done. Model cost-optimization fluency: choosing the right model for the job, managing token and compute spend, and tuning for cost, latency, and quality. Solid software engineering fundamentals — Python and/or TypeScript, APIs, version control, testing, and CI/CD. Comfort operating independently and shipping with minimal oversight in a fast-moving environment. Nice to Have Experience with agent evaluation and observability tooling (eval harnesses, tracing, prompt and version management). Background in management consulting, operations, or business-process reengineering. Familiarity with Azure AI services, Azure OpenAI, and broader Azure data and integration services. Experience integrating agents with enterprise systems (CRM, ERP, ticketing, data warehouses). Prior work in a professional-services or client-delivery setting. What Success Looks Like (First 90 Days) Delivered at least one agentic build into production on Azure that automates a previously manual process. Produced clear current- and future-state process maps that guided the build. Established a repeatable, cost-aware pattern for standing up new agents within the existing orchestration ecosystem.