Interested in this AI Architect role at Chenega Corporation?
Apply Now →Skills & Technologies
About This Role
Overview: Lead AI/ML Architect Remote
Are you ready to enhance your skills and build your career in a rapidly evolving business climate? Are you looking for a career where professional development is embedded in your employer’s core culture? If so, Chenega Military, Intelligence & Operations Support (MIOS) could be the place for you! Join our team of professionals who support large-scale government operations by leveraging cutting-edge technology and take your career to the next level!
The Lead AI/ML Architect is responsible for identifying, designing, and governing Artificial Intelligence (AI) and Machine Learning (ML) solutions that enhance the GSAFleet.gov platform and support GSA Fleet's strategic objective of workforce augmentation, automation, analytics, and data-driven decision-making. The role is a member of the Program Management Team and serves as the senior advisor for AI strategy, architecture, and implementation. Responsibilities:
- Discover and prioritize Fleet AI/ML use cases for analytics, intelligent workflows, automation, and workforce support.
- Evaluate each use case for data readiness, mission value, feasibility, dependencies, risk, and operating cost.
- Design model, data pipeline, interface, monitoring, quality, governance, security, and deployment requirements.
- Present solution options and cost-benefit recommendations to GSA IT and Fleet business stakeholders.
- Coordinate with the Data Architect, Solution Architect, ISSO, and product teams to implement approved capabilities.
- Measure deployed solution performance and business outcomes and recommend refinements.
- Other duties as assigned
Qualifications:
- Bachelor’s Degree in a related field
- 5+ years of progressively responsible experience designing, developing, and deploying enterprise-level Artificial Intelligence/Machine Learning (AI/ML) solutions.
- Must be able to undergo a Tier 2S Investigation and receive a favorable investigation/adjudication outcome
- Successfully pass background and drug screening
Preferred Qualifications:* Delivered AI/ML for vehicle utilization, maintenance, telematics, transaction anomalies, or fleet demand forecasting
- Relevant cloud AI/ML credentials (Certified Machine Learning Engineer – Associate, AWS Certified Solutions Architect – Associate, AWS Certified Solutions Architect – Professional)
Knowledge, Skills and Abilities:* Proven ability to lead and drive multiple parallel, complex customer AI/ML solution-design efforts from inception through solution recommendation/proposal.
- Demonstrated expertise conducting comprehensive business and technical discovery with diverse customers to identify strategic needs and high-impact opportunities for AI/ML adoption.
- Demonstrated experience identifying, proposing, and architecting novel AI/ML solutions, architectures, and use cases, including areas such as predictive modeling, natural language processing, computer vision, and generative AI.
- Demonstrated ability to present complex AI/ML solutions and data-driven recommendations to senior technical and business stakeholders, combining business acumen with technical expertise and connecting proposed solutions to measurable program outcomes.
- Demonstrated ability to develop robust, defensible, data-driven recommendations for AI/ML engagements, including proactively identifying and mitigating critical dependencies, risks, and gaps such as data quality and model governance.
- Demonstrated experience designing interface standards, quality assurance standards, and performance standards for highly available and reliable AI/ML systems.
- Demonstrated experience conducting rigorous cost-benefit analyses and Total Cost of Ownership (TCO) assessments for modern enterprise AI/ML systems.
- Deep analytical knowledge of AI/ML technologies, platforms, and frameworks, such as TensorFlow, PyTorch, AWS SageMaker, Azure Machine Learning, Google Vertex AI, or comparable technologies.
- Demonstrated ability to evaluate AI/ML technology alternatives and make strategic, data-driven recommendations regarding technology selection and optimal implementation
How you’ll grow At Chenega MIOS, our professional development plan focuses on helping our team members at every level of their careers to identify and use their strengths to do their best work every day. From entry-level employees to senior leaders, we believe there’s always room to learn.
We offer opportunities to help sharpen skills in addition to hands-on experience in the global, fast-changing business world. From on-the-job learning experiences to formal development programs, our professionals have a variety of opportunities to continue to grow throughout their careers. Benefits At Chenega MIOS, we know that great people make a great organization. We value our team members and offer them a broad range of benefits. Learn more about what working at Chenega MIOS can mean for you. Chenega MIOS’s culture Our positive and supportive culture encourages our team members to do their best work every day. We celebrate individuals by recognizing their uniqueness and offering them the flexibility to make daily choices that can help them be healthy, centered, confident, and aware. We offer well-being programs and continuously look for new ways to maintain a culture where we excel and lead healthy, happy lives. Corporate citizenship Chenega MIOS is led by a purpose to make an impact that matters. This purpose defines who we are and extends to relationships with our clients, our team members, and our communities. We believe that business has the power to inspire and transform. We focus on education, giving, skill-based volunteerism, and leadership to help drive positive social impact in our communities. Learn more about Chenega’s impact on the world. Chenega MIOS News- https://chenegamios.com/news/ Tips from your Talent Acquisition Team We want job seekers exploring opportunities at Chenega MIOS to feel prepared and confident. To help you with your research, we suggest you review the following links: Chenega MIOS web site - www.chenegamios.com Glassdoor - https://www.glassdoor.com/Overview/Working-at-Chenega-MIOS-EI_IE369514.11,23.htm LinkedIn - https://www.linkedin.com/company/1472684/ Facebook - https://www.facebook.com/chenegamios/ Teleworking Permitted?: true Teleworking Details: Remote Estimated Salary/Wage: USD $166,500.00/Yr. Up to USD $190,000.00/Yr.
Salary Context
This $166K-$190K range is below the median for AI Architect roles in our dataset (median: $183K across 30 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 8,943 AI roles we're tracking, AI Architect positions make up 1% of the market. At Chenega Corporation, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $230,000 based on 212 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($178K) sits 22% below the category median. Disclosed range: $166K to $190K.
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.
Chenega Corporation AI Hiring
Chenega Corporation has 2 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span Washington, DC, US, Atlanta, GA, US. Compensation range: $120K - $190K.
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 Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.