Senior AI Engineer

CLA (CliftonLarsonAllen)•Madison, WI

About The Position

CLA is a top 10 national professional services firm where our purpose is to create opportunities every day, for our clients, our people, and our communities through industry-focused wealth advisory, digital, audit, tax, consulting, and outsourcing services. Even with more than 8,500 people, 130 U.S. locations, and a global reach, we promise to know you and help you. CLA is dedicated to building a culture that invites different beliefs and perspectives to the table, so we can truly know and help our clients, communities, and each other. CLA is currently seeking a Senior AI Engineer to join our growing CLA Digital - Data and Automation Team. The Senior AI Engineer will lead the design and implementation of production-grade AI solutions across machine learning, optimization, and generative AI. This role is ideal for someone who can translate business problems into scalable, reliable technical solutions that perform in real-world environments. You will work closely with AI leadership while providing day-to-day technical guidance to junior team members. This position blends applied machine learning, software engineering, cloud architecture, and end-to-end solution delivery. Success in this role requires a strong understanding that production AI involves far more than model development—it includes evaluation, observability, integration, governance, and operational excellence. About the role: AI Solution Development & Architecture Lead the implementation of production-ready AI systems across predictive modeling, optimization, and LLM-powered applications Design end-to-end architectures including data pipelines, APIs, model services, orchestration layers, and monitoring systems Build and deploy AI workflows within Azure and Databricks environments Develop robust evaluation frameworks for both ML models and LLM-based systems Design and implement AI applications with strong grounding, safety, evaluation, and cost controls Build AI workflows including tool integration, memory systems, and orchestration logic Implement model routing, fallback strategies, and guardrails Develop context and memory systems (retrieval, summarization, session continuity) Evaluation, Safety & Reliability Establish robust evaluation frameworks for ML and LLM systems Define and monitor: Task success metrics and regression testing Hallucination and grounding performance Safety risks (prompt injection, data leakage) Implement observability practices including logging, tracing, and monitoring Ensure system reliability through testing, deployment standards, and incident readiness Technical Leadership Translate ambiguous business needs into clear technical designs and delivery plans Provide mentorship and technical oversight to junior engineers Lead architecture reviews, code reviews, and technical design discussions Establish engineering standards across testing, CI/CD, deployment, and monitoring Cross-Functional Collaboration Partner with product, engineering, security, and business stakeholders Support solution design, feasibility assessments, and delivery planning Contribute to proposals, technical narratives, and client-facing engagements Core Responsibilities Own major technical workstreams for AI delivery from design through deployment Build scalable data and model pipelines for batch and real-time use cases Lead development of LLM-based applications with strong grounding, evaluation, safety, and cost controls Implement classical AI and advanced analytics approaches including forecasting, anomaly detection, optimization, recommendation, and decision support Define and implement MLOps and LLMOps standards including versioning, deployment, monitoring, and rollback strategies Design secure and supportable integrations across enterprise systems, APIs, and data platforms Evaluate tradeoffs across tools, frameworks, and architecture choices in Azure and Databricks Troubleshoot complex issues in production environments across data, infrastructure, and application layers Drive technical quality and ensure solutions are maintainable, scalable, and aligned to client needs Support business development by contributing to solution framing, estimates, and technical narratives

Requirements

2 years of relevant experience required
5–7 years of experience in AI engineering, machine learning, or software engineering preferred
Strong proficiency in Python and production-grade development practices preferred
Proven experience deploying ML/AI systems into production environments preferred
Experience designing APIs, pipelines, and service-oriented architectures preferred
Strong understanding of model evaluation, experimentation, and performance tradeoffs preferred
Ability to work independently and mentor junior team members
Strong communication skills across technical and non-technical audiences

Responsibilities

Lead the implementation of production-ready AI systems across predictive modeling, optimization, and LLM-powered applications
Design end-to-end architectures including data pipelines, APIs, model services, orchestration layers, and monitoring systems
Build and deploy AI workflows within Azure and Databricks environments
Develop robust evaluation frameworks for both ML models and LLM-based systems
Design and implement AI applications with strong grounding, safety, evaluation, and cost controls
Build AI workflows including tool integration, memory systems, and orchestration logic
Implement model routing, fallback strategies, and guardrails
Develop context and memory systems (retrieval, summarization, session continuity)
Establish robust evaluation frameworks for ML and LLM systems
Define and monitor: Task success metrics and regression testing Hallucination and grounding performance Safety risks (prompt injection, data leakage)
Implement observability practices including logging, tracing, and monitoring
Ensure system reliability through testing, deployment standards, and incident readiness
Translate ambiguous business needs into clear technical designs and delivery plans
Provide mentorship and technical oversight to junior engineers
Lead architecture reviews, code reviews, and technical design discussions
Establish engineering standards across testing, CI/CD, deployment, and monitoring
Partner with product, engineering, security, and business stakeholders
Support solution design, feasibility assessments, and delivery planning
Contribute to proposals, technical narratives, and client-facing engagements
Own major technical workstreams for AI delivery from design through deployment
Build scalable data and model pipelines for batch and real-time use cases
Lead development of LLM-based applications with strong grounding, evaluation, safety, and cost controls
Implement classical AI and advanced analytics approaches including forecasting, anomaly detection, optimization, recommendation, and decision support
Define and implement MLOps and LLMOps standards including versioning, deployment, monitoring, and rollback strategies
Design secure and supportable integrations across enterprise systems, APIs, and data platforms
Evaluate tradeoffs across tools, frameworks, and architecture choices in Azure and Databricks
Troubleshoot complex issues in production environments across data, infrastructure, and application layers
Drive technical quality and ensure solutions are maintainable, scalable, and aligned to client needs
Support business development by contributing to solution framing, estimates, and technical narratives