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About This Role
Overview:
GovCIO is currently hiring for a Software Engineer – AI Applications & ServiceNow to design, build, deploy, operate, and maintain secure, production-grade applications incorporating artificial intelligence (AI), machine learning (ML), and generative AI capabilities within U.S. Government cloud environments. The successful candidate will be a hands-on software engineer capable of taking AI-enabled solutions from concept and prototype through production deployment and sustained operations. This position requires experience engineering applications within security-conscious cloud environments, including FedRAMP Moderate and FedRAMP High environments and Microsoft Government Community Cloud High (GCC High). The engineer will also support the design, development, integration, and enhancement of ServiceNow-based solutions, including integrations between ServiceNow, AI services, enterprise applications, data sources, and government cloud services. This is a remote, full-time position within the United States. Responsibilities: AI-Enabled Application Engineering* Design, develop, test, deploy, and maintain production-grade applications incorporating generative AI, machine learning, and related AI capabilities.
- Develop secure AI-enabled applications using large language models (LLMs), AI APIs, retrieval-augmented generation (RAG), agentic workflows, and other emerging AI application architectures.
- Integrate AI services with enterprise applications, databases, APIs, document repositories, knowledge bases, and workflow platforms.
- Develop application services, APIs, user interfaces, data pipelines, and integration components necessary to support enterprise AI solutions.
- Implement appropriate AI application controls including identity and access management, data protection, model access controls, prompt and output handling, logging, monitoring, and auditability.
- Evaluate and integrate commercial, open-source, and cloud-native AI technologies where appropriate.
- Implement mechanisms for AI solution evaluation, testing, observability, performance monitoring, reliability, and continuous improvement.
- Troubleshoot complex application, integration, data, infrastructure, and AI service issues across development and production environments.
Cloud, DevSecOps, and Production Operations* Deploy and operate applications within FedRAMP Moderate, FedRAMP High, and GCC High environments.
- Engineer solutions in accordance with federal security requirements and organizational security architecture.
- Support secure software development lifecycle (SSDLC) and DevSecOps practices, including automated build, test, security scanning, deployment, and release management.
- Develop and maintain CI/CD pipelines supporting controlled application deployment across development, test, staging, and production environments.
- Implement infrastructure-as-code and configuration-as-code practices where appropriate.
- Support application security controls aligned with applicable frameworks such as NIST SP 800-53 and FedRAMP security control baselines.
- Participate in vulnerability remediation, patching, configuration management, logging, monitoring, incident response, and operational troubleshooting.
- Produce technical documentation and evidence supporting security assessments, Authorization to Operate (ATO) activities, configuration management, and operational readiness.
- Engineer applications for high availability, reliability, scalability, maintainability, and secure operations.
ServiceNow Engineering* Design, configure, develop, integrate, and maintain ServiceNow applications and workflows.
- Develop ServiceNow solutions using capabilities such as:
- + Flow Designer + IntegrationHub + REST and SOAP APIs + Business Rules + Script Includes + Client Scripts + UI Policies + Access Control Lists (ACLs) + ServiceNow tables and data models + Notifications and workflow automation
- Develop integrations between ServiceNow and external enterprise, cloud, security, data, and AI services.
- Apply ServiceNow development best practices to create maintainable, secure, upgrade-compatible solutions.
- Support troubleshooting, performance optimization, testing, release management, and ongoing maintenance of ServiceNow solutions.
- Experience with ServiceNow AI, automation, ITSM, ITOM, CMDB, or related capabilities is highly desirable.
Qualifications:
- Bachelor's with 5 - 8 years (or commensurate experience)
- Demonstrated professional software engineering experience developing and supporting enterprise applications.
- Hands-on experience developing AI-enabled or generative AI applications suitable for production use.
- Experience with modern programming languages such as Python, Java, JavaScript/TypeScript, C#, or comparable languages.
- Experience developing RESTful APIs, microservices, web applications, or distributed application architectures.
- Experience integrating applications with AI/ML services, LLMs, enterprise data sources, or external APIs.
- Experience deploying, operating, troubleshooting, and maintaining applications in cloud environments.
- Understanding of FedRAMP Moderate and/or FedRAMP High security requirements and experience working within regulated or security-controlled cloud environments.
- Knowledge of secure software engineering, DevSecOps, CI/CD, source control, automated testing, vulnerability management, and production application support.
- Experience engineering or supporting ServiceNow applications, integrations, workflows, or platform capabilities.
- Strong understanding of application security principles including authentication, authorization, encryption, secrets management, secure APIs, logging, and auditing.
- Ability to independently investigate complex technical problems and develop practical, secure solutions.
- Strong written and verbal communication skills with the ability to document architectures, designs, interfaces, operational procedures, and technical decisions.
- U.S. citizenship is required.
- Must be able to obtain and maintain a U.S. Government Public Trust clearance.
Preferred Skills and Experience* Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, or a closely related technical discipline.
- Experience working with Microsoft Azure Government and/or Microsoft GCC High environments.
- Experience with Azure AI services, Azure OpenAI, or comparable government-authorized AI platforms.
- Experience developing retrieval-augmented generation (RAG), semantic search, vector database, AI agent, or intelligent workflow solutions.
- Experience implementing AI evaluation, observability, guardrails, model monitoring, prompt management, or responsible AI controls.
- Experience with containerized applications and technologies such as Docker and Kubernetes.
- Experience with infrastructure-as-code tools such as Terraform, Bicep, ARM templates, or equivalent technologies.
- Experience supporting systems subject to NIST SP 800-53, FedRAMP, FISMA, or similar federal cybersecurity requirements.
- Experience supporting ATO processes and producing technical or security documentation for government systems.
- ServiceNow Certified System Administrator (CSA), Certified Application Developer (CAD), or other relevant ServiceNow certifications.
- Experience with ServiceNow ITSM, ITOM, CMDB, IntegrationHub, App Engine, Now Assist, or related platform capabilities.
- Experience working within Agile, Scrum, or DevSecOps delivery teams.
- Experience supporting applications throughout the complete lifecycle from architecture and development through deployment, sustainment, modernization, and retirement.
Technical Competencies
Strong candidates will have experience across several of the following areas:AI & Application Development:
Python, JavaScript/TypeScript, Java, C#, REST APIs, microservices, LLM APIs, RAG, vector search, AI agents, prompt engineering, AI evaluation, and enterprise application integration.Cloud & DevSecOps:
Azure/Azure Government, GCC High, containers, Kubernetes, CI/CD, Git, automated testing, infrastructure as code, secrets management, observability, application monitoring, vulnerability scanning, and secure deployment pipelines.ServiceNow:
ServiceNow application development, Flow Designer, IntegrationHub, scripting, APIs, ACLs, workflow automation, ITSM, ITOM, CMDB, and enterprise integrations.Federal Security:
FedRAMP Moderate, FedRAMP High, NIST SP 800-53, secure software development, configuration management, vulnerability remediation, continuous monitoring, audit logging, security documentation, and ATO support. Success in This Role
The successful candidate will be able to move comfortably between software engineering, AI engineering, ServiceNow development, cloud deployment, cybersecurity, and production operations. This individual will not simply develop prototypes; they will be expected to engineer solutions that can be securely deployed, accredited, operated, monitored, maintained, and continuously improved within demanding U.S. Government environments. The ideal candidate is a pragmatic engineer who understands that successful enterprise AI requires more than model integration—it requires secure architecture, reliable software, governed data, operational discipline, effective monitoring, and maintainable engineering.
#nss #tlm #TMK #TM
Posted Salary Range: USD $100,000.00 - USD $130,000.00 /Yr.
Salary Context
This $100K-$130K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $193K across 172 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At GovCIO, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($115K) sits 48% below the category median. Disclosed range: $100K to $130K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
GovCIO AI Hiring
GovCIO has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Kearneysville, WV, US, Remote, US. Compensation range: $85K - $130K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
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