AI Engineer (Applied AI & Data Systems)

United StatesRemoteFull Timesenior

Machine Learning EngineerData

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Quick Summary

Overview

Mitek (NASDAQ: MITK) is a global leader in digital & biometric identity authentication, fraud prevention, and mobile deposit solutions.

Technical Tools

Machine Learning EngineerData

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.

What We Offer

~1 min read

We are looking for an AI Engineer with a strong foundation in software engineering, data engineering, or machine learning, and hands-on experience building modern AI systems. This role is best suited for someone who has built production software or data-intensive systems and has more recently expanded into large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI systems.

Additional/optional benefits: pet insurance, identity theft protection, legal assistance

We sincerely appreciate your interest in Mitek. We know your time is valuable and look forward to the potential of speaking with you further!

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 enjoys building reliable production systems, collaborating across engineering, product, and business teams, and is excited to continue growing as an AI engineer.

Strong software engineering fundamentals, curiosity, and a willingness to learn are just as important as prior AI experience. We're looking for someone who enjoys solving complex engineering problems, works well within real-world business and regulatory constraints, and is excited to help build the next generation of AI capabilities at Mitek.

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.

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, deployment, monitoring, and continuous improvement. Success in this role requires balancing innovation with reliability while working closely with engineering, product, legal, and business stakeholders to build AI systems that are trustworthy, measurable, and production-ready.

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.

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 have a growth mindset, take ownership of your work, learn from mistakes, and actively seek feedback to continuously improve your thinking, your systems, and your results.

You enjoy collaborating across engineering, product, and business teams and are comfortable working within real-world technical, business, and regulatory constraints to deliver reliable AI solutions.

You are comfortable working in ambiguity, asking questions, challenging assumptions, and learning from those around you while helping solve complex engineering problems.

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.