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About This Role
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
Business Summary
Deltek’s award winning Support Services team provides best\-in\-class assistance to Deltek’s customers across the world via phone, chat and email. Our team is comprised of a group of diverse, collaborative and passionate professionals who come from varying industries, backgrounds and professions. Our diversity and passion is our strength, so however you identify and whatever background you bring, we invite you to explore our team as a potential next step in your career!
Position Responsibilities
Deltek's Customer Success organization is seeking a seasoned technical leader to own and drive its most critical engineering and AI initiatives. This is a hands\-on leadership role not a purely managerial one. You will be expected to roll up your sleeves, contribute technically, and simultaneously build and guide a high\-performing team. If you thrive at the intersection of cutting\-edge technology and customer outcomes, this role is built for you.
Technical Responsibilities* Lead the end\-to\-end design, development, and delivery of key technical and AI initiatives within the Customer Success function.
- Define the technical roadmap for CS tooling, automation, and AI\-powered capabilities that improve customer health, retention, and time\-to\-value.
- Architect and oversee integrations between Deltek's product suite, CRM/CS platforms (e.g., Salesforce, Gainsight), and internal data systems.
- Drive adoption of AI and automation to scale Customer Success operations including intelligent escalation routing, churn prediction models, and AI\-assisted support workflows.
- Conduct rigorous code and design reviews, establishing and enforcing engineering standards across the team.
- Evaluate and introduce new tools, frameworks, and technologies that improve team velocity and solution quality.
- Partner closely with Product, Engineering, Data Science, and IT teams to ensure alignment and avoid redundancy.
Leadership Responsibilities* Build, mentor, and manage a team of engineers and technical specialists supporting the Customer Success organization.
- Define team goals, establish clear performance expectations, and create development pathways for individual contributors.
- Own the technical program management of AI and CS platform initiatives including planning, resourcing, dependency tracking, and executive reporting.
- Foster a culture of technical excellence, continuous learning, and customer\-first thinking within the team.
- Serve as the primary technical voice and thought leader for Customer Success, representing the function in cross\-organizational planning and architecture reviews.
- Partner with Customer Success leadership to identify opportunities where technology and AI can drive measurable improvements in customer outcomes and operational efficiency.
- Champion engineering best practices, including agile delivery, documentation standards, and incident management processes.
Qualifications
Technical Qualifications* Education: Bachelor's or Master's degree in Computer Science, Computer Engineering, or a closely related technical discipline is required.
- Experience: 10–15 years of progressive software development experience, with a strong foundation in building, shipping, and maintaining production\-grade systems.
- Hands\-on coding: Active, demonstrable coding proficiency is a must. You are comfortable writing, reviewing, and debugging code and do not rely solely on your team to execute technical work.
- Software Engineering Depth: Deep expertise in one or more of the following: backend systems, cloud\-native architectures, APIs \& integrations, or data engineering pipelines.
- AI \& Machine Learning: Practical experience designing and delivering AI/ML\-powered solutions including Agent development, LLM integration, intelligent automation, predictive analytics, or AI\-driven tooling. Familiarity with frameworks such as LangChain, OpenAI APIs, or similar platforms is a strong plus.
- Cloud \& Infrastructure: Proficiency with cloud platforms (AWS, Azure, OSC or GCP), containerization (Docker/Kubernetes), and CI/CD pipelines.
- Data \& Analytics: Ability to work fluently with data querying, modeling, and interpreting results to inform both technical decisions and customer success strategies.
- Systems Thinking: Strong ability to evaluate existing architectures, identify gaps, and propose scalable, maintainable solutions particularly in the context of customer\-facing systems and tooling.
- Security \& Compliance Awareness: Working knowledge of software security best practices and data privacy considerations relevant to enterprise SaaS environments.
Leadership Qualifications* Management Experience: 3–5\+ years of experience managing software engineers or technical teams, with a track record of developing talent and delivering results.
- Stepping Up: Currently operating as a Technical Manager or Senior Manager, with demonstrated readiness and ambition to grow into an Associate Director or Director\-level role.
- Strategic Mindset: Able to translate broad business and customer success goals into a coherent technical strategy with clear milestones and measurable outcomes.
- Cross\-Functional Influence: Comfortable engaging with senior stakeholders, including VP\-level Customer Success leaders, product owners, and engineering executives, to align priorities and secure resources.
- Communication: Exceptional ability to communicate complex technical concepts clearly to non\-technical audiences, and to articulate business value in technical terms to engineering teams.
- Accountability: Operates with a strong sense of ownership, proactively identifies risks, escalates appropriately, and drives issues to resolution without being prompted.
US Citizenship is required for this position.
Compensation Info
The U.S. salary range for this position is $102,500\.00\-$180,500\.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.
Position Type
FT
Travel Requirements
10%
Compliance Requirements
Certain roles may have additional privacy, security and compliance requirements to the extent they support Costpoint GCCM or similar product offerings.
EEO Statement
*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.*
E\-Verify Statement
Deltek, Inc., utilizes the E\-Verify program with every potential new hire. This makes it possible for us to make certain that every employee who works for Deltek is eligible to work in the United States. To learn more about E\-Verify you can call 1\-800\-255\-7688 or visit their website by clicking the logo below. E\-Verify® is a registered trademark of the United States Department of Homeland Security.
Applicant Privacy Notice
*Deltek is committed to the protection and promotion of your privacy. In connection with your application for employment with us at Deltek, it is necessary for us to collect, store and use information about you (“Personal Data”) to administer and evaluate your application. We are the “controller” of the Personal Data you provide us and will process any such Personal Data in accordance with applicable law and the statements contained in this* Employment Candidate Privacy Notice*. Additionally, we have not sold and do not sell Personal Data you provide to us through the job application process.*
Salary Context
This $102K-$180K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($141K) sits 15% below the category median. Disclosed range: $102K to $180K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Deltek AI Hiring
Deltek has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $163K - $180K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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