Sr. AI Engineer (Applied AI & ML Systems)

Mitek (NASDAQ: MITK) is a global leader in digital & biometric identity authentication, fraud prevention, and mobile deposit solutions. Our verified identity platform and advanced image capture solutions are built on the latest advancements in biometric recognition, artificial intelligence, computer vision and machine learning, and trusted by over 7,500 organizations worldwide. We are headquartered in San Diego, California, with operations in the United Kingdom, Spain, France, Mexico, and the Netherlands. Visit us at www.miteksystems.com. We are Virtual 1st! Whether you choose to work remotely from your home office or in-person from one of Mitek’s offices, our practices, processes and tools are designed to enable your success. At Mitek, the Future of Work is about flexibility and preference wherever and whenever we are working. Because we care about our candidates, employees and customers, we include an in-person meeting as part of our hiring process. It’s one of the ways we live our mission to “Protect What’s Real.” At Mitek, we believe that teams are more resilient, effective, and innovative when they benefit from a wide range of ideas, lived experiences, and perspectives. The strength of our organization is deeply rooted in the people who power it.​ We know that a workforce reflecting the richness of our communities and customers helps us better serve their needs. Summary We are looking for an AI Engineer with a strong foundation in machine learning (ML), data engineering, or both, and hands-on experience building modern AI systems. This role is best suited for someone who started their career in ML, applied modeling, data engineering, or software engineering for data-intensive systems and later expanded into large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI systems. We are looking for someone with an evaluation-first mindset who believes AI systems should be designed with clear success criteria, testing strategies, and monitoring plans from the start. The ideal candidate brings strong ML or data systems fundamentals, experience building LLM-powered applications, and practical experience designing and operating production-grade AI solutions that solve real business problems. This includes building multi-step AI workflows, integrating AI into enterprise systems, and balancing quality, latency, cost, reliability, and maintainability. Humility, accountability, and a growth mindset are essential for success in this role. The right candidate is comfortable admitting mistakes, learning from feedback, challenging assumptions, and adjusting quickly when evidence suggests a better path forward. Why This Role Matters This role matters because we need more than someone who can build AI features. We need someone who can build AI systems in a thoughtful and reliable way. That means starting with a clear plan for how quality, risk, and business impact will be measured, and carrying that through design, launch, monitoring, and ongoing improvement. What You’ll Do (Essential Responsibilities): Design, build, and deploy AI solutions powered by ML, LLMs, and agentic AI systems that solve real business problems. Define evaluation strategies upfront for each use case, including task success metrics, offline and online evaluation plans, error analysis, and production monitoring requirements. Build and improve LLM-based systems using prompt engineering, retrieval-augmented generation (RAG), context engineering, and multi-step agentic workflows. Partner closely with product, engineering, data, and business stakeholders to prioritize AI use cases and align on success metrics, operational requirements, and delivery timelines. Apply strong production practices across AI systems, including experimentation, versioning, observability, alerting and continuous improvement in production Monitor, troubleshoot, and improve production AI systems by balancing quality, latency, cost, reliability, and maintainability. Who You Are (Soft Skills & Attributes): You bring an evaluation-first mindset and believe AI systems should not be designed or implemented without a clear plan to measure quality, risk, and business impact. You are thoughtful, practical, and systems-oriented, with sound judgment about when to experiment, when to simplify, when to stop, and when to productionize. You take ownership of outcomes, learn from mistakes, and use feedback and new evidence to continuously improve your thinking, your systems, and your results. You are comfortable working in ambiguity, asking questions, challenging assumptions, and collaborating across technical and non-technical teams to solve complex problems. What You'll Need (Required Knowledge, Skills & Abilities): Bachelor's degree in Computer Science or a related field, and knowledge, skills, and abilities typically associated with 6+ years of relevant experience, including: 4+ years of experience in one or more of the following areas: Machine Learning or Applied Mod