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About This Role
CLA is a top 10 national professional services firm wur 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
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- 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
What you will need:
- 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
\#LI\-JH1
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
Wellness at CLA
To support our CLA family members, we focus on their physical, financial, social, and emotional well\-being and offer comprehensive benefit options that include health, dental, vision, 401k and much more.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At CliftonLarsonAllen, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
CliftonLarsonAllen AI Hiring
CliftonLarsonAllen has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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