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Overview:
ServiceNow Developer (AI \& Intelligent Automation Focus) Springfield, VA
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!
Chenega Agile Real\-Time Solutions (CARS) was created with the purpose of providing integrated enterprise IT support to Federal customers both CONUS and OCONUS. CARS employs Subject Matter Experts (SMEs) with decades of experience working in the Federal marketplace.
Chenega Agile Real\-Time Solutions (CARS) is seeking a highly skilled ServiceNow Developer (AI \& Intelligent Automation Focus) to serve as a senior technical leader responsible for architecting, developing, and advancing enterprise capabilities on the ServiceNow platform. This role will support mission\-critical initiatives focused on AI\-enabled automation, intelligent service management, and AIOps\-driven operational modernization.
The ServiceNow Developer (AI \& Intelligent Automation Focus) will design and implement scalable platform solutions that leverage automation, predictive analytics, machine learning integrations, and intelligent workflow orchestration to enhance operational efficiency and mission performance. The position works closely with architects, engineers, and mission stakeholders to translate complex operational requirements into modern digital workflows and automated service capabilities.
This role will help drive the adoption of AI\-assisted operations, intelligent incident management, automated remediation, and predictive service monitoring by integrating ServiceNow platform capabilities with external tools, data platforms, and emerging AI technologies. The Senior Developer will provide technical leadership across development teams, establish development standards, and ensure solutions align with enterprise architecture, security, and compliance requirements.
In addition to hands\-on development, the role will support platform innovation initiatives, including AIOps integration, intelligent automation pipelines, workflow orchestration, and enterprise data\-driven decision support. The ServiceNow Developer (AI \& Intelligent Automation Focus) will mentor junior developers, collaborate across engineering teams, and serve as the senior escalation point for complex technical challenges.
Responsibilities:
- Lead hands\-on development within the ServiceNow platform, including client scripts, business rules, UI actions, UI policies, data policies, workflow automation, Flow Designer, Integration Hub spokes, Script Includes, catalog items, Service Portal widgets, UI pages, and event/alert rules.
- Design and implement integrations between ServiceNow and external enterprise systems, monitoring tools, and operational platforms using REST, SOAP, APIs, and integration frameworks.
- Design and implement AI\-enabled automation workflows that support predictive operations, automated incident response, and intelligent service management.
- Support the integration of AIOps capabilities, including event correlation, anomaly detection, predictive incident management, and automated remediation workflows.
- Collaborate with platform architects and engineering teams to integrate machine learning services, analytics platforms, and intelligent decision engines into ServiceNow workflows.
- Develop solutions that leverage ServiceNow AI features and automation capabilities to enhance operational awareness, reduce manual intervention, and improve service delivery efficiency.
- Support initiatives involving data\-driven insights, predictive service monitoring, and intelligent operational dashboards.
- Ensure all development activities adhere to enterprise architecture, cybersecurity requirements, and platform governance standards.
- Work closely with product owners, business stakeholders, and engineering teams to analyze requirements and translate them into scalable technical solutions.
- Participate in Agile ceremonies, including sprint planning, backlog refinement, stand\-ups, sprint reviews, and retrospectives.
- Provide technical estimates and solution designs to support product roadmap planning and program execution.
- Other duties as assigned.
Qualifications:
- High school diploma or GED equivalent and relevant experience:
+ 5\+ years of experience in ServiceNow Platform development
+ 3\+ years of experience in ITSM development
+ Experience in Agile development practices
- ServiceNow CSA Certification
- Active Top\-Secret Clearance with ability to obtain SCI / CI poly
Preferred Qualifications:* Bachelor’s or master’s degree in computer science, Information Technology, Engineering, or related field
- Experience supporting AIOps, intelligent automation, or AI\-enabled operational platforms
- Experience integrating ServiceNow with monitoring tools, data platforms, or operational analytics systems
- Experience supporting enterprise IT modernization initiatives within federal or defense environments
- Experience with enterprise integration technologies, including REST APIs, microservices, and automation frameworks
- Experience supporting large\-scale enterprise IT environments
- Certified Technical Architect (CTA) is preferred
Knowledge, Skills, and Abilities:* Ability to work independently and yet be effective within a team setting
- Must be capable of managing multiple efforts with time\-related constraints in a fast\-paced contracting environment
- Demonstrated ability to effectively communicate and collaborate with diverse internal and external stakeholder groups and individuals
- Friendly presence, helpful attitude, good interpersonal skills, and ability to work well with others.
- Excellent skills in Microsoft Word, Excel, and other Office applications
- Proficient with Microsoft Office Applications, and experience working in a home office setting, as well as the ability to train end users on frequently asked technical issues.
- Ability to provide technical assistance and support over the phone; good phone skills, professional demeanor, and previous customer service experience strongly desired.
- Good problem\-solving skills; ability to visualize a problem/situation and think abstractly to solve it
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/
\#Chenega Agile Real Time Solutions, LLC
Teleworking Permitted?: false
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Chenega Corporation, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Chenega Corporation AI Hiring
Chenega Corporation has 5 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, Research Scientist. Positions span Arlington, VA, US, Springfield, VA, US, Fort Detrick, MD, US. Compensation range: $90K - $90K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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