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About This Role
- Job ID: 11080BR
- Location: US (Remote)
- Work Setup: Remote
- Job Category: Information Technology
- Posting Date: June 25, 2026
About the Role
Ready to build AI solutions that reach production? As an AI Solutions Architect (Forward Deployed) within Deltek’s AI Center of Excellence, you’ll design, develop, and ship AI\-enabled solutions across Deltek’s systems and business workflows—from multi\-agents, MCP, A2A, RAG, Vector DB, AI Search, guardrails, along with Agentic AI frameworks, automations and integrations. The AI COE sits at the intersection of enterprise strategy and hands\-on execution, and this role is where that execution happens. You’ll work directly with business and technical teams to translate real problems into AI solutions that are secure, scalable, and built to last. This is a hands\-on builder role: you design and build reusable AI frameworks, write code, deliver working AI systems, and establish the technical patterns that others will follow. You’ll also serve as a technical reviewer in the AI solution approval process, evaluating solutions developed across the enterprise against enterprise standards for security, scalability, and governance. This role reports to the Senior Director, AI Solutions.
Key Responsibilities
- Design, build, and deploy end\-to\-end AI solutions aligned to business use cases—agents, automations, and workflow integrations—across Deltek’s platforms and systems
- Write production\-quality code, configure tools, and develop directly using enterprise AI platforms, APIs, and data services; move solutions from prototype to production
- Define data flows, integration points, and operational requirements that meet enterprise standards for security, performance, and reliability
- Establish development standards, reusable patterns, and best practices that accelerate AI solution development across business and technical teams
- Contribute to the design and build of enterprise AI platforms, including integration, orchestration, and deployment components
- Execute technical reviews of AI solutions developed by business teams, assessing alignment with enterprise architecture, security, and governance standards, and providing clear, actionable guidance
- Partner with governance teams to translate AI policies into technical controls and apply them consistently through the review process
- Work directly with business stakeholders to identify, shape, and deliver high\-value AI use cases; serve as the bridge between operational need and technical execution
Required Qualifications
- 8–12\+ years of experience in software engineering, solution architecture, or systems integration, with significant recent experience (3\+ years) building and deploying Agentic AI, Gen AI or AI/ML solutions in production environments
- Demonstrated hands\-on experience developing AI solutions using a range of modern technologies including LLMs, SLMs, RAG, multi\-agent frameworks, MCP, A2A, Vector DB, AI Search, generative AI platforms or copilot architectures
- Strong command of APIs, enterprise integrations, and data workflows; able to design and implement end\-to\-end data flows across complex system landscapes
- Proven track record defining development standards or establishing technical best practices adopted by other teams
- Demonstrated experience reviewing or guiding AI solutions built by developers or business teams, with the ability to identify risk and communicate technical findings clearly to non\-technical stakeholders
- Excellent communication skills with the ability to work fluidly across technical teams and business functions
US Citizen or Green Card Holder Only
Preferred Qualifications
- Experience contributing to or building enterprise AI platforms or shared technical services
- Familiarity with AI governance, responsible AI practices, and security considerations in production AI environments
- Experience working across both technical and citizen developer communities, including low\-code or automation platforms
- Advanced degree in Computer Science, Engineering, or a related technical field
- Certifications in cloud platforms (AWS, Azure, GCP) or AI/ML specializations
The U.S. salary range for this position is $124,500\.00\-$219,500\.00\. This range is subject to change as Deltek takes a number of factors into consideration when determining individual base pay, such as location, job\-related knowledge, skills and experience. Certain roles are eligible for additional rewards, including incentive compensation and equity.
Benefits and perks listed here may vary depending on the nature of employment with Deltek. Employees have access to healthcare benefits, a 401(k) plan and company match, paid vacation time and holidays, well\-living programs, short\-term and long\-term disability coverage, basic life insurance and tuition reimbursement.
As the recognized global standard for project\-based businesses, Deltek delivers software and information solutions to help organizations achieve their purpose. Our market leadership stems from the work of our diverse employees who are united by a passion for learning, growing and making a difference. At Deltek, we take immense pride in creating a balanced, values\-driven environment, where every employee feels included and empowered to do their best work. Our employees put our core values into action daily, creating a one\-of\-a\-kind culture that has been recognized globally. Thanks to our incredible team, Deltek has been named one of America’s Best Midsize Employers by Forbes, a Best Place to Work by Glassdoor, a Top Workplace by The Washington Post and a Best Place to Work in Asia by World HRD Congress. www.deltek.com
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*Deltek, Inc. is an Equal Opportunity / Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or protected veteran status.*
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Salary Context
This $124K-$219K range is below the median 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 Deltek, 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 ($172K) sits 21% below the category median. Disclosed range: $124K to $219K.
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.
Deltek AI Hiring
Deltek has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Herndon, VA, US. Compensation range: $111K - $219K.
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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