AI Engineer (Applied AI & Data Systems)

Mitek Systems is a global leader in digital and biometric identity authentication, fraud prevention, and mobile deposit solutions. They are seeking an AI Engineer with a strong foundation in software engineering and data engineering to design and build AI systems that are reliable and production-ready.

Responsibilities:

• Design, build, and deploy AI solutions powered by LLMs, RAG, and agentic AI systems that solve real business problems
• Contribute to defining evaluation strategies for AI use cases, including task success metrics, offline and online evaluation plans, error analysis, and production monitoring
• Build and improve LLM-based applications using retrieval-augmented generation (RAG), context engineering, prompt engineering, and multi-step agentic workflows
• Collaborate with senior engineers to design, implement, and operate reliable production AI systems that balance quality, latency, cost, and maintainability
• 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 software engineering and production practices across AI systems, including testing, versioning, observability, monitoring, and continuous improvement
• Monitor, troubleshoot, and improve AI systems in production by analyzing system performance, identifying failures, and implementing practical improvements

Requirements:

• Bachelor's degree in Computer Science or a related field, and knowledge, skills, and abilities typically associated with 4+ years of relevant experience, including:
• 2+ years of experience in one or more of the following areas: Software Engineering for data-intensive systems, Data Engineering, Machine Learning or Applied Modeling
• 2+ years of experience building LLM-based applications, including at least 1 year designing and implementing agentic AI systems as part of that experience
• Hands-on experience building LLM-powered applications, including context engineering, retrieval-augmented generation (RAG), evaluation frameworks, and prompt engineering
• Experience designing and implementing agentic AI systems, including multi-step workflows that incorporate planning, memory, tool orchestration, handoffs, and human-in-the-loop review
• Experience designing, building, or supporting production software systems, data pipelines, APIs, or large-scale data platforms
• Experience defining evaluation strategies and operating AI systems in production, including deployment, monitoring, observability, versioning, and continuous improvement
• Strong Python programming skills and experience taking AI solutions from prototype to production while balancing quality, latency, cost, reliability, and maintainability
• Experience with vector databases, graph databases, retrieval quality tuning, and optimization of LLM-powered applications
• Experience contributing to reusable AI platforms, shared services, internal tooling, or engineering frameworks that improve AI development speed, consistency, and reuse
• Experience deploying AI applications in cloud environments and working with distributed systems, scalable inference services, or production AI infrastructure
• Experience working with evaluation frameworks, model monitoring, observability, or human-in-the-loop workflows for production AI systems