Sr. AI Engineer (Applied AI & ML Systems)
About The Position
Mitek is seeking 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 ideal for someone who began their career in ML, applied modeling, data engineering, or software engineering for data-intensive systems and has since expanded into large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI systems. The position requires an evaluation-first mindset, emphasizing the design of AI systems with clear success criteria, testing strategies, and monitoring plans from inception. The ideal candidate possesses strong ML or data systems fundamentals, experience building LLM-powered applications, and practical experience designing and operating production-grade AI solutions to address real business challenges. This includes constructing multi-step AI workflows, integrating AI into enterprise systems, and optimizing for quality, latency, cost, reliability, and maintainability. Humility, accountability, and a growth mindset are crucial for success. The right candidate will be comfortable admitting errors, learning from feedback, questioning assumptions, and adapting quickly when presented with evidence for a better approach. This role is significant because it requires more than just building AI features; it demands the creation of AI systems in a thoughtful and dependable manner, starting with a clear plan for measuring quality, risk, and business impact throughout the design, launch, monitoring, and ongoing improvement phases. The company is looking for someone to help establish the engineering foundations necessary for AI systems to operate safely, reliably, and at scale across ML, LLM, and agentic AI applications.
Requirements
- Bachelor's degree in Computer Science or a related field, and knowledge, skills, and abilities typically associated with 6+ years of relevant experience.
- 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.
- Experience building and operating production data pipelines, data platforms, or large-scale 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.
- Hands-on experience building LLM-powered applications, including context engineering, retrieval-augmented generation (RAG), evaluation frameworks, prompt engineering and optimization.
- 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.
Nice To Haves
- 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.
- Experience with model fine-tuning is preferred but not required.
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.
- Design and implement reliable data, retrieval, and orchestration layers that support production AI systems, including data quality, governance, observability, and monitoring.
- Apply strong MLOps and LLMOps practices across AI systems, including experimentation, versioning, observability, alerting, model and prompt evaluation, and continuous improvement in production.
- Partner closely with product, engineering, data, and business stakeholders to prioritize AI use cases and align on success metrics, operational requirements, and delivery timelines.
- Monitor, troubleshoot, and improve production AI systems by balancing quality, latency, cost, reliability, and maintainability.
Benefits
- Competitive salary ranges aligned to industry standards
- Up to a 10% annual incentive bonus
- Comprehensive benefits package
- Wellness: Universal, supplemental, and private healthcare plan choices based on country specifics
- Financial future: retirement/pension plan contributions, MTK stock plan participation
- Income protection: life event & disability coverage
- Paid time off: generous annual leave, company holidays, volunteer time off
- Learning: e-learning license, tuition reimbursement, hackathons
- Home office setup allowance
- Additional/optional benefits: pet insurance, identity theft protection, legal assistance
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What This Job Offers
Job Type
Full-time
Career Level
Senior
