Sr. AI Engineer (Applied AI & ML 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

~2 min read

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.

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!

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.

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.

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.

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 Modeling
Data Engineering
Software Engineering for Data-Intensive Systems.
2+ years of experience building LLM-based applications, including at least 1 year building agentic AI systems as part of that experience.
Experience building and operating production data pipelines, data platforms, or large-scale data-intensive systems.
Hands-on experience building LLM-powered applications, including context engineering, retrieval-augmented generation (RAG), evaluation frameworks, prompt engineering and optimization. Experience with model fine-tuning is preferred but not required.
Experience designing and implementing agentic AI systems, including multi-step workflows that incorporate planning, memory, handoffs, tool orchestration, and human-in-the-loop review.
Strong track record of defining evaluation strategies upfront and operating AI systems in production, including deployment, monitoring, observability, versioning, experimentation, and continuous improvement.
Advanced Python 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 domain-specific optimization for LLM-based systems.

Experience designing reusable AI platforms, shared services, internal tooling, or infrastructure that improves AI development speed, consistency, and reuse.

Experience with cloud-native AI deployment, distributed systems, and scalable serving infrastructure for ML, LLM, and agentic AI applications.