AI Engineer

Company DescriptionCustom Learning Designs (CLD) is a sales training agency that exclusively works with leading pharmaceutical, biotech, and medical device companies to ensure their commercial teams have the right knowledge at the right time to help them transform patients' lives. Role DescriptionIn this AI Engineer position, you'll design and build agentic systems that meaningfully accelerate how our writing team works. You'll operate at the frontier of what's possible with AI tooling today, and you'll be expected to push that frontier further. Position Type: Contract Pay range: $100 - $150/hour What You'll Do • Architect and build multi-step agentic workflows that integrate with medical writing processes, document pipelines, and templates • Leverage async and multi-session orchestration tools to manage complex, long-running agent tasks • Own context engineering: design prompts, memory structures, retrieval patterns, and agent scaffolding that produce reliable, high-quality output • Stay current on the rapidly evolving agentic engineering landscape and bring new approaches into the stack proactively • Collaborate closely with medical writers, product, and QA to understand workflow pain points and translate them into automatable solutions • Contribute to code reviews, architecture decisions, and internal documentation What We're Looking For Experience and Background • 5+ years of professional software engineering experience. We'll consider less with compelling proof of work (shipped projects, open source, demos that speak for themselves) • Foundational proficiency in JavaScript, Python, or Ruby. Solid foundation in at least one Agentic Engineering • Hands-on experience and active daily use of AI coding tools like Claude Code or OpenAI Codex • Experience moving up the agentic complexity ladder: from single-turn completions to multi-step agents, async flows, and parallel session management • Familiarity with orchestration tools for managing concurrent or long-running agent sessions • Awareness of the current agentic engineering landscape: frameworks like Pi, Letta, and emerging patterns in agent memory, handoffs, and tool use • Strong intuition around context engineering. You understand how to structure information for models, manage token budgets, and design retrieval systems that actually work • Experienced designing and running evals for agentic systems, including task-specific benchmarks, failure mode analysis, and iterative prompt/architecture improvements driven by eval feedback rather than intuition Nice to Have • Experience in regulated industries and/or AI governance initiations • Familiarity with document automation and/or structured authoring • Contributions to open-source AI or agentic tooling • Experience building internal developer tools or platforms