Sr. AI Engineer

Mitek Systems is a global leader in digital and biometric identity authentication, fraud prevention, and mobile deposit solutions. They are seeking a Senior AI Engineer with a strong machine learning background to design, build, and deploy AI solutions that address clear business problems and improve operational efficiency. The ideal candidate will have experience with ML, LLMs, and agentic AI systems, and will work closely with product and engineering teams to ensure successful AI implementation.

Responsibilities:

• Design, build, and deploy AI solutions powered by ML, LLMs, and agentic AI systems that address clear 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, and multi-step workflows
• Apply MLOps and LLMOps practices, including experimentation, versioning, observability, alerting, model and prompt evaluation, and continuous improvement in production
• Partner closely with product, engineering, and business stakeholders to prioritize AI use cases and align on success metrics, operational needs, and delivery timelines

Requirements:

• Bachelors' degree in Computer Science or related field, and knowledge, skills and abilities typically associated with 6+ years of total relevant experience across ML and modern AI systems including:
• 4+ years of hands-on experience in machine learning
• 2+ years building LLM-based applications, 1 of which consists of building agentic AI systems as part of that LLM application experience
• Expertise in ML, applied modeling, or NLP, including model development, evaluation, experimentation, and error analysis
• Hands-on experience building LLM-based applications, including context engineering, retrieval, evaluation frameworks, and model fine-tuning
• Experience designing and implementing agentic AI systems, including multi-step workflows that use planning, memory, handoffs, tool orchestration, and human-in-the-loop review
• Strong experience with MLOps for ML systems, including model lifecycle management, deployment, monitoring, retraining, and production success metrics
• Strong experience with LLMOps for LLM-based applications, including prompt and workflow versioning, retrieval and response evaluation, observability, guardrails, and continuous improvement in production
• Advanced Python skills and experience taking AI solutions from prototype to production while balancing quality, latency, cost, reliability, and maintainability
• Experience with vector and graph databases, retrieval quality tuning, and domain-specific optimization for LLM-based systems
• Experience with platform design, reusable components, and internal tooling that improves AI development speed and reuse
• Experience with cloud-based AI deployment and scalable serving infrastructure for ML or LLM systems