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About This Role
- Job ID: 11149BR
- Location: US (Remote)
- Work Setup: Remote
- Job Category: Sales
- Posting Date: July 30, 2026
- Own end\-to\-end GTM workflows: diagnose how a sales or marketing process works today and redesign it so an AI agent can run it more efficiently and reliably
- Design, configure, and maintain AI agents supporting sales and marketing workflows, primarily using low\-code/no\-code AI platforms (e.g., Lindy) with light scripting or API configuration as needed
- Implement prompt logic, multi\-step workflows, and agent orchestration based on business needs and product requirements
- Connect AI agents with internal systems, data sources, and GTM platforms using platform\-native integrations and APIs
- Partner closely with the GTM Operations AI Manager to execute against defined priorities and delivery timelines
- Monitor agent performance and troubleshoot issues related to quality, reliability, and data integration
- Iterate on agent logic and workflows to improve consistency, accuracy, and usability
- Design for scale: build workflows that hold up across large data volumes and many users, not just a handful of records or a single team
- Document logic, workflows, and implementation details to support maintainability and scale
- Stay current on AI tooling capabilities, platform updates, and applied AI best practices
### *Required*
- 3–6 years of relevant operations/automation experience in a business operations function (sales operations, revenue operations, marketing operations, customer success/support operations, or similar) where you owned and improved processes and workflows
- Hands\-on experience building, configuring, or scaling automation or workflow tools in an operations context (e.g., Lindy, Workato, Make, Zapier, n8n, or comparable low\-code/no\-code platforms)
- A demonstrated appetite for AI and an instinct to build with it: you have started configuring or automating with AI tools (even simple, imperfect attempts count), and you are not someone who only uses AI to get answers or draft text
- Experience building or operating in environments with genuine scale: large data volumes and/or many users, such as a larger, established company
- Working knowledge of data integration concepts, APIs, and structured data formats (e.g., JSON, SQL, CSV); comfortable reading and troubleshooting these, not necessarily writing code from scratch
- Demonstrated ability to implement solutions from defined requirements and iterate based on feedback and observed performance
- Strong problem\-solving skills, with the ability to troubleshoot workflow issues, optimize outputs, and improve system reliability
- Bachelor’s degree in a technical or business\-related field
- Comfort operating in fast\-paced, evolving environments with changing requirements and incomplete information
### *Preferred (Applied AI Depth)*
- Hands\-on experience building AI\-powered workflows or agents: configuring prompts, agent logic, and multi\-step reasoning flows
- Experience working with LLM\-based systems or prompt\-driven workflows, and familiarity with agent orchestration or decision\-based AI outputs
- Experience connecting AI or automation solutions with business systems such as CRM, customer success, marketing automation, analytics, or operations platforms
- GTM domain knowledge: lead routing, enrichment, territory or pipeline operations, sales forecasting, or sales/marketing reporting
### *Attributes*
- Builder mindset with a strong bias toward execution and iteration
- Business judgment: able to step back from the immediate task to understand and solve the underlying business problem, and to propose a better approach rather than simply executing a spec
- Ownership mentality: able to independently diagnose why a workflow isn’t performing, form a hypothesis, and iterate toward a fix without waiting for direction
- Fast learner: can pick up unfamiliar tools and concepts quickly and get functional without hand\-holding
- Detail\-oriented, with a focus on reliability, quality, and usability
- Curious and motivated to learn emerging AI tools and techniques
- Comfortable working with ambiguity and evolving technical approaches
- Collaborative and communicative, with the ability to work effectively across technical and non\-technical teams
Growth \& Opportunity
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This role offers hands\-on ownership of applied AI workflow design in a production GTM environment. As Deltek’s GTM AI capabilities mature, this role can expand into deeper ownership of agent architecture, cross\-functional AI initiatives, or broader AI operations leadership, depending on interests and organizational needs.
The U.S. salary range for this position is $100,000\.00\-$120,000\.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.
20%
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
The Deltek Global Sales team has a passion for empowering project\-based businesses to achieve their goals. We relentlessly focus on our customers’ needs and strive to deliver an exceptional experience for all clients. If you are an enthusiastic, motivated professional who enjoys building and nurturing relationships – join our highly collaborative team to help power project success for our customers.
Certain roles may have additional privacy, security and compliance requirements to the extent they support Costpoint GCCM or similar product offerings.
*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.*
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.
*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 $100K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($110K) sits 49% below the category median. Disclosed range: $100K to $120K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Deltek AI Hiring
Deltek has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Herndon, VA, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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