Senior AI Engineer

Optimal Dynamics is a high-growth company focused on redefining logistics decisions through innovative AI solutions. The Senior AI Engineer will lead the development and scaling of AI capabilities, establishing engineering standards and delivering production systems that leverage company and customer data.

Responsibilities:

• 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

Requirements:

• 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
• Experience introducing retrieval-augmented or knowledge-grounded AI capabilities in a product or platform context
• Exposure to evaluation methodologies for AI systems (quality, latency, cost, safety) and running experiments/A-B tests
• Background working with unstructured data pipelines, indexing, and search, whether homegrown or via managed services
• Experience mentoring engineers, uplifting practices, and creating reusable playbooks and templates
• Experience in the transportation, logistics, or broader supply chain industry
• Customer-facing collaboration comfort: gathering requirements, scoping MVPs, and measuring ROI post-launch