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Company Overview: Work Where it Matters
Suvi, an Akima company, is not just another federal mission services contractor. As an Alaska Native Corporation (ANC), our mission and purpose extend beyond our exciting federal projects as we support our shareholder communities in Alaska.
At Suvi, the work you do every day makes a difference in the lives of our 15,000 Iñupiat shareholders, a group of Alaska natives from one of the most remote and harshest environments in the United States.
For our shareholders, Suvi provides support and employment opportunities and contributes to the survival of a culture that has thrived above the Arctic Circle for more than 10,000 years.
For our government customers, Suvi delivers subject matter experts, an agile management approach, and innovative technologies that accomplish customers’ missions safely, securely, and efficiently.
As a Suvi employee, you will be surrounded by a challenging, yet supportive work environment that is committed to innovation and diversity, two of our most important values. You will also have access to our comprehensive benefits and competitive pay in addition to growth opportunities and excellent retirement options.
Description:
The Office of Inspector General (OIG), Office of the Chief Information Officer (OCIO), provides enterprise IT services to approximately 2,000 end users across more than 60 global locations. OCIO sustains and modernizes critical IT capabilities that enable the OIG mission, including database services, mobile device management, IT asset management, domain services, networking infrastructure, and service desk operations.
The Business Analyst/Product Analyst – Foundations and AI plays a key role in translating Government business, operational, platform-foundation, data, and AI needs into actionable ServiceNow requirements, priorities, and backlog items. This role supports foundational platform improvements, AI-readiness activities, requirements development, process documentation, backlog management, and alignment with mission objectives.
Responsibilities:
- Conduct stakeholder interviews, discovery sessions, and requirements workshops to collect, analyze and report on business, operational, and AI-related needs.
- Document current-state and future-state business processes, workflows, data interactions, and operational roles.
- Translate requirements into well‑structured epics, features, user stories, and acceptance criteria aligned with ServiceNow best practices.
- Identify platform-foundation use cases, data requirements, and AI-enabled opportunities that support mission outcomes.
- Analyze business value, readiness, feasibility, and dependencies for proposed functionality and AI capabilities.
- Maintain requirements traceability across development, testing, and acceptance cycles.
- Document current- and future-state processes that depend on CSDM, CMDB, knowledge, and AI capabilities.
- Collaborate with Government product owners to prioritize backlog items and refine scope based on mission priorities.
- Prepare and support sprint demonstrations, user acceptance testing, and release readiness activities.
- Support multi-year capability roadmaps and detailed use-case roadmaps from current state to desired outcomes.
- Coordinate with architects, developers, cybersecurity teams, and Government stakeholders to align requirements with enterprise standards.
- Identify risks, gaps, constraints, and opportunities related to business needs, data readiness, and AI integration.
Qualifications:
- Active Secret clearance.
- IAT Level II certification (e.g., Security+ CE or equivalent).
- Bachelor’s degree in business, IT, or related field.
- 6+ years of experience in business analysis, product analysis, requirements development, or IT service management.
- 5+ years of experience supporting enterprise ITSM/ITOM or ServiceNow programs in a business or product analyst capacity.
- Demonstrated ability to document processes, develop requirements, and translate business needs into technical specifications.
- Experience developing user stories, acceptance criteria, and backlog items within Agile or SAFe frameworks.
- Strong analytical skills with the ability to evaluate business value, readiness, and dependencies for new capabilities.
- Excellent communication and facilitation skills, with experience leading workshops, interviews, and stakeholder engagements.
Preferred Qualifications:
- Experience supporting AI readiness, workflow intelligence, or data-maturity initiatives on ServiceNow.
- ServiceNow-related certifications (CSA, CAD, ITSM, ITOM, CSDM, or product owner).
- IIBA CBAP/CCBA or PMI-PBA certification
- Experience supporting federal government IT modernization efforts.
- Familiarity with CMDB, CSDM models, enterprise data structures, and workflow design.
Work Schedule: This opportunity may offer hybrid and/or remote flexibility, providing options to work onsite, remotely, or a combination of both based on business and team requirements.
"This position is contingent upon contract award and funding. Applicants selected may be offered but cannot begin work until funding has been confirmed."
Benefits Information: Regular - The company offers a comprehensive benefits program, including medical, dental, vision, life insurance, 401(k) and a range of other voluntary benefits. Paid Time Off (PTO) is offered to regular full-time and part-time employees. Pay Range: $140,000 - $160,000 Job ID: 2026-26000 Work Type: Hybrid
Salary Context
This $140K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Akima, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($150K) sits 29% below the category median. Disclosed range: $140K to $160K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Akima AI Hiring
Akima has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Alexandria, VA, US. Compensation range: $160K - $194K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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