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About This Role
Job Description
This is a full\-time, entry\-level position within Howell's AI Technology Group, reporting to the Chief Technology Officer, IT Lead, and Chief Revenue Officer.
The AI \& Automation Specialist will be Howell's first dedicated resource for identifying, building, and implementing AI\-driven workflows and automations across the business, beginning with the areas of greatest immediate need, Estimating and Project Management, before extending into Business Development and other functions. This person will partner closely with department leaders to understand existing processes, learn where automation and AI tools add the most value, and build practical solutions using approved platforms. This is a hands\-on, entry\-level role for a recent computer science graduate who is curious, adaptable, and eager to make a visible impact.
This is also a ground\-up role. There is little existing infrastructure or precedent to build from, so the right candidate will enjoy the challenge of learning about how our business functions to create processes and systems from scratch rather than maintaining something already in place.
Over time, this role has a defined path toward growing into a leadership position within Howell's IT function, for the right person who demonstrates strong technical judgment, initiative, and business impact.
Key Responsibilities
● Partner with the Estimating, Project Management, and Business Development teams, in that order of priority, to map current workflows and identify high\-value opportunities for automation.
● Create Howell’s MCP server and incorporate data from Howell’s ERP, Project Management platform and other data sources using an SDK that is best for Howell.
● Work with various team members to learn what they do and what they need to design, build, and deploy AI\-assisted workflows and automations that reduce manual effort and improve accuracy and speed.
● Evaluate and pilot AI tools and platforms in coordination with company leadership, providing recommendations based on hands\-on testing.
● Once tools and platforms are selected, provide ongoing support and maintenance to keep them running reliably as business needs evolve; this becomes a growing share of the role over time.
● Apply security and data\-privacy best practices when building AI solutions, ensuring sensitive company and client information is handled responsibly.
● Document new workflows and train staff on how to use and maintain them.
● Serve as an internal resource for questions about AI capabilities, best practices, and responsible use across the company.
● Track the impact of implemented automations and report results to executive leadership.
● Expand automation efforts to additional business functions, prioritizing accounting and field operations before HR and other areas, as the program matures.
● Support broader IT initiatives as needed, building toward an expanded role within the IT function over time.
Minimum Requirements
● Bachelor's degree in Computer Science, Information Systems, or a related field (recent graduates welcome and encouraged to apply).
● Strong foundational programming and problem\-solving skills.
● Hands\-on experience with modern AI platforms is required, such as large language models (e.g., Claude), agentic AI, or workflow\-oriented automation tools.
● Familiarity with workflow automation concepts; experience with tools such as Power Automate, Zapier, or similar is a plus but not required.
Candidates should exhibit the following characteristics:
● Excellent communication and interpersonal skills, with the ability to translate technical concepts for non\-technical colleagues.
● Self\-starter mindset with strong curiosity, adaptability, and a bias toward practical, working solutions.
● A team player who demonstrates a strong service mindset, bringing a helpful, solutions\-oriented attitude to every team they support. At Howell, that means collaborating at all levels and doing right by colleagues, consistent with our core values.
● Comfortable working on\-site in a collaborative, in\-office environment in Denver, CO. Partial remote work could be a future opportunity depending on how well the AI integration process develops.
What Success Looks Like
In the first year, success means building trust with the Estimating, Project Management, and Business Development teams, delivering workflow automations that produce measurable time savings or accuracy improvements, and establishing yourself as a credible, go\-to resource for AI\-related questions across the company.
Growth Opportunity
This position is designed to grow. As Howell's automation program matures and expands across additional business functions, there is a genuine opportunity for this role to evolve into leadership of the company's IT function. While the exact timeline depends on individual performance and business needs, candidates who excel in this role can typically expect conversations about growth within their first two years.
Interview Process
As part of the interview process, candidates should be prepared to walk through specific AI\-related projects they've built, along with their experience with everyday IT systems. Expect behavioral questions (for example, "tell me about a time when...") to help us understand how you approach problem\-solving and collaboration.
Who is Howell Construction?
Since 1935, Howell Construction has helped shape the Colorado Front Range through disciplined, high\-performance building rooted in trust, transparency, and follow\-through. As a locally rooted commercial general contractor, we deliver complex and operationally sensitive projects across healthcare, civic \& government, education, science \& technology, and commercial markets with the steady leadership and accountability clients count on.
At Howell, we believe great performance starts with preparation, honest communication, and people who genuinely care about the outcome. Our teams lead with humility, collaborate without ego, and stay closely connected to every project from preconstruction through closeout. We combine the technical expertise and systems of a larger builder with the responsiveness, flexibility, and hands\-on leadership of a relationship\-driven Colorado company.
We are proud of the culture we’ve built \- one grounded in servant leadership, integrity, discipline, and a commitment to doing the right thing. At Howell, we don’t just build projects. We build trust, long\-term partnerships, and outcomes our clients and teams can stand behind.
We stand by our core values:
- Always Great Performance
- Servant Leadership
- Do the Right Thing
- Love What We Do
Pay: $65,000\.00 \- $75,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Parental leave
- Professional development assistance
- Retirement plan
- Vision insurance
Work Location: In person
Salary Context
This $65K-$75K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Howell Construction, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($70K) sits 67% below the category median. Disclosed range: $65K to $75K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Howell Construction AI Hiring
Howell Construction has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $75K - $75K.
Location Context
AI roles in Denver pay a median of $199,950 across 66 tracked positions. That's 7% below the national 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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