Senior Artificial Intelligence Engineer, Optimal Dynamics

• Experienced: a Senior, hands-on engineer who can take a fuzzy problem from discovery to reliable production outcomes
• Pragmatic: You balance quality, cost, speed, and risk to ship value iteratively
• Innovative: Comfortable choosing and evolving the stack as needs change; you create guardrails and standards along the way
• Communicative: You have the ability to align stakeholders and translate complex technical ideas into clear decisions
• Operational Owner: You care about reliability, observability, and measurable impact on customers and the business
• 4+ years of proven industry experience building and operating backend, platform, or data services at production scale
• Bachelor's degree in Computer Science, Electrical Engineering, Operations Research, or Mathematics/Physics
• Proven track record delivering data or ML-powered features end-to-end (Discovery/Prototyping > Launch > Iteration)
• Python Proficiency - you write well-tested & maintainable code
• API design expertise and experience with cloud services (preferably AWS)
• Familiarity with information retrieval concepts and grounding AI systems in trustworthy data
• Comfort setting technical direction, selecting tools, and establishing engineering best practices for AI-focused builds

Desirable

• We’re looking for a Senior AI Engineer to lead the 0→1 buildout and 1→n scaling of AI capabilities at Optimal Dynamics. You’ll set direction, establish engineering standards, and deliver production systems that ground AI outputs in trustworthy company and customer data. This is a hands-on role with broad ownership where you’ll make key architecture and tooling choices and evolve them as we learn
• Own RAG initiatives end-to-end from problem framing and data readiness through prototyping, iteration, and production launch
• Establish foundational components and practices for document processing, indexing, retrieval, orchestration, and evaluation; selecting tools and approaches that balance quality, cost, and speed
• Build reliable services and APIs that are observable, secure, and designed for scale in a cloud environment
• Define success metrics (quality, latency, cost, safety) and drive continuous improvement via experimentation and data-driven decisions
• Create durable team assets (i.e. playbooks, test harnesses, checklists, and documentation) to make RAG development repeatable across products
• Collaborate cross-functionally with Product, Data/ML, Engineering, and GTM to translate ambiguous needs into shippable capabilities with clear business impact
• Mentor teammates and contribute to a strong engineering culture around AI systems and responsible deployment