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About This Role
Summary
The Project Controls role serves as the subject matter expert for project controls within the subsidiary, responsible for providing oversight, guidance, and independent review of project management practices and project financial performance. This role ensures consistency, accuracy, and discipline in project controls, including cost management, forecasting, reporting, and adherence to contractual requirements.
The position works closely with Project Managers and operational leadership to review project data, challenge assumptions, identify risks, and improve overall project performance. While not directly responsible for project execution, this role provides critical oversight and accountability to ensure projects are managed in alignment with company expectations and industry best practices.
This is a highly collaborative role that operates across project teams, finance, and leadership, strengthening controls, reinforcing standards, and driving improved visibility and decision\-making.
The salary range for this position is $86,189\-$144,228 per year.
This position comes with a competitive and comprehensive benefits package, including medical, dental, and vision insurance, life insurance, a 401(k) plan with a 3% match, paid time off (PTO), and company\-paid holidays.
*Please Note:**This posting is intended to build our talent pool for future opportunities. Although there is not an immediate opening for this position, applications will be kept on file and reviewed when opportunities become available.*
This position is telecommute eligible.
Essential Duties and Responsibilities (including but not limited to)
- Serve as the subject matter expert for project controls, including planning, scheduling, cost management, forecasting, and reporting.
- Review and assess project budgets, forecasts, Estimate at Completion (EAC), and Estimate to Complete (ETC) for accuracy, completeness, and reasonableness.
- Validate project financial and operational reporting, including cost tracking, revenue recognition, and progress measurement.
- Review and evaluate project schedules (e.g., CPM schedules), ensuring alignment with scope, sequencing, and contractual requirements.
- Identify variances, trends, and risks across cost, schedule, and performance, and provide actionable recommendations to leadership.
- Provide independent oversight of project performance, ensuring adherence to established processes, controls, and contract requirements.
- Challenge assumptions, forecasts, schedules, and reporting presented by project teams to ensure realistic and supportable projections.
- Support project startup through review of project setup, cost structures, work breakdown structures, and reporting frameworks.
- Partner with Project Managers to strengthen schedule discipline, forecasting accuracy, and overall project controls practices.
- Provide focused oversight on complex, high\-risk, or high\-visibility projects, including recovery planning and risk mitigation strategies.
- Support change management processes, including evaluation of impacts to cost, schedule, and overall project performance.
- Support the development and continuous improvement of project controls processes, tools, and reporting practices within the subsidiary.
- Drive consistency in project controls practices through reinforcement, coaching, and alignment with established standards.
- Identify gaps in project controls execution and recommend improvements to strengthen operational performance and predictability.
- Develop and maintain reporting that provides leadership with visibility into project financial health, risks, and trends.
- Coordinate with Finance and Shared Services to ensure alignment in reporting, forecasting, and financial data.
- Provide leadership with analysis, insights, and recommendations to support informed decision\-making.
- Maintain clear communication with project teams, operations, and executive leadership.
- Perform other duties as assigned
Supervision
May provide functional guidance and oversight to project teams or project controls staff; no direct supervisory responsibility required.
Minimum Qualifications
- Bachelor’s degree in Construction Management, Engineering, Business Administration, Finance, or related field.
+ Equivalent combination of education and progressively responsible project controls or project financial management experience on a one\-to\-one basis.
- Ten (10\) years of experience in project controls, including cost, schedule, and performance management within construction, environmental, or infrastructure projects.
- Demonstrated experience serving as a subject matter expert, lead, or advisor in project controls or project performance oversight.
- Strong knowledge of project controls practices, including CPM scheduling, cost management, forecasting, and progress measurement.
- Experience reviewing and validating project schedules, financials, and performance reporting.
- Strong understanding of contract types (e.g., fixed price, cost reimbursable, time and materials) and associated controls.
- Experience working directly with Project Managers and operational leadership in a project\-driven environment.
- Proficiency in scheduling and project systems (e.g., Primavera P6, Microsoft Project) and financial/reporting systems.
- Valid driver’s license and clean driving record.
Preferred Qualifications
- Experience supporting federal government contracting projects and familiarity with applicable regulations.
- Experience supporting complex, multi\-project portfolios in construction, environmental, or infrastructure environments.
- Experience developing or improving project controls processes, systems, or reporting frameworks.
- Experience with earned value management (EVM) or advanced project performance metrics.
- Experience working within a multi\-entity or shared services organizational structure.
- Experience with data visualization or reporting tools (e.g., Power BI or similar).
- Knowledge and experience within Alaska Native cultures.
Shareholder Preference
Pursuant to applicable law, preference will be given to qualified Ahtna Native Corporation Shareholders, Descendents, and Spouses in all phases of employment.
Core Competencies
- Project Controls Expertise – Deep knowledge of cost controls, forecasting, and project financial management.
- Independent Judgment \& Oversight – Evaluates and challenges project performance with sound judgment.
- Analytical Thinking – Identifies trends, risks, and opportunities through data analysis.
Process Discipline \& Improvement – Drives consistency and continuous improvement in practices.
- Business Acumen – Understands financial drivers, contract structures, and operational impacts.
- Collaboration \& Influence – Successfully influences without direct authority.
- Communication – Clearly communicates complex information to technical and operational audiences.
Working Environment
Generally, an indoor office environment with frequent interruptions, often noisy, high\-paced atmosphere, with tendencies of having an extremely heavy workload. This role routinely uses standard office equipment such as computers, phones, photocopiers, filing cabinets and fax machines.
Physical Demands (including but not limited to)
Frequent sitting for prolonged periods of time, using hands/fingers requiring dexterity and coordination to handle files and single pieces of paper, reaching with hands and arms for items above and below desk level, talking, hearing, and seeing (up close, at a distance, along the periphery, with depth perception, and the ability to adjust focus); walking from place to place within the office with occasional use of stairs and no elevator available; bending, pushing, pulling, and standing for up to 2 hours; occasional lifting of up to 25 pounds such as small office equipment, files, stacks of paper, reference and other materials.
Work is generally performed in an office setting with the ability to speak and receive phone communications often. Work requires computer usage with strength, dexterity, coordination, and visual acuity to use keyboard and video display terminal, and other office equipment
*Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.*
Work Schedule: Monday – Friday, 8 am – 5 pm or as business/operational needs dictate
#### Experience
Required* 10 year(s): or related field. o Equivalent combination of education and progressively responsible project controls or project financial management experience on a one\-to\-one basis. • Ten (10\) years of experience in project controls, including cost, schedule, and performance management within construction, environmental, or infrastructure projects. • Valid driver’s license and clean driving record.
#### Education
Preferred* Bachelors or better in Construction Management or related field
#### Licenses \& Certifications
Required* Driver's License
#### Skills
Required* MS Office
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $86K-$144K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Ahtna, Incorporated, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($115K) sits 47% below the category median. Disclosed range: $86K to $144K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Ahtna, Incorporated AI Hiring
Ahtna, Incorporated has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Anchorage, AK, US. Compensation range: $76K - $168K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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