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About This Role
About Agile Defense
At Agile Defense we know that action defines the outcome and new challenges require new solutions. That’s why we always look to the future and embrace change with an unmovable spirit and the courage to build for what comes next.
Our vision is to bring adaptive innovation to support our nation's most important missions through the seamless integration of advanced technologies, elite minds, and unparalleled agility—leveraging a foundation of speed, flexibility, and ingenuity to strengthen and protect our nation’s vital interests. Requisition #:
Job Title for Careers Page: Software Architect – AI-Enabled Enterprise Platform
Location: Remote (preferred local to the DMV area)
Clearance Level: Ability to obtain a Secret clearance
Required Certification(s): None
SUMMARY
Agile Defense is seeking a hands-on Software Architect to own the architecture and technical direction of Workforce, an AI-enabled enterprise application platform focused on secure, user-centered workflows, enterprise integrations, and cloud-native delivery. Workforce is built as a set of containerized microservices deployed to Azure Kubernetes Service (AKS) in Azure Government Community Cloud (GCC), combining modern web application development, API-driven services, AI-assisted capabilities, and secure deployment practices.
The Software Architect is the senior technical owner of the platform. This person sets architectural direction, leads the engineering team’s technical execution, serves as Release Manager for the platform’s multi-service release train, and partners with the Product Manager to turn roadmap priorities into executable plans. This is a hands-on role: the Software Architect is expected to stay close to the code, regularly designing, implementing, reviewing, and debugging software alongside the team.
JOB DUTIES AND RESPONSIBILITIES
Architecture and Technical Direction
- Own the architecture, technical direction, and engineering standards for the Workforce platform and its microservices.
- Define and document service boundaries, API contracts, data flows, integration patterns, and key technical decisions, including the tradeoffs behind them.
- Lead the design of the platform’s AI-enabled architecture, including tool calling, system instructions, context management, prompt and control flow design, and integration with LLM-based services.
- Design secure enterprise integrations, including identity, document workflows, collaboration tools, and external platform APIs.
- Identify and manage technical debt, platform risk, and architectural drift, making pragmatic decisions about when to refactor and when to ship.
Technical Leadership and Delivery
- Lead technical execution for the Workforce engineering team, including solution design, task breakdown, estimation, sequencing, and code review.
- Partner with the Product Manager to translate product goals, user workflows, and requirements into technical designs, milestones, and well-scoped engineering work.
- Establish and uphold standards for code quality, automated testing, documentation, and definition of done.
- Mentor engineers through design reviews, pairing, code reviews, and direct technical feedback.
- Serve as the primary technical point of contact for engineering leadership, product, and customer stakeholders on status, risk, and technical tradeoffs.
Release Management and Operations
- Serve as Release Manager for the platform’s multi-service release train, coordinating release readiness, release notes, and release records.
- Represent the team in change control and release review processes, presenting changes, risk, and rollback plans for approval.
- Own production posture for the platform, including rollback decisions, incident triage, root cause follow-through, and post-release verification.
- Collaborate with platform and DevOps engineers on CI/CD pipelines, Helm-based deployments to AKS, observability, autoscaling, and performance testing.
Hands-On Engineering
- Contribute directly to the codebase, including feature implementation, critical-path work, integration work, and production fixes.
- Build and maintain full stack capabilities across modern web front ends and backend web APIs.
QUALIFICATIONS
Required Certifications
None required.
Education, Background, and Years of Experience
- Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent professional experience.
- 10+ years of professional software engineering experience building and maintaining production applications.
- 5+ years designing and owning production application architecture, including leading technical execution for an engineering team as a software architect, technical lead, or senior technical owner.
- Ability to obtain and maintain a Secret security clearance.
ADDITIONAL SKILLS & QUALIFICATIONS
Required Skills
- Strong hands-on coding ability in at least one object-oriented, strongly typed language such as Java, C#, TypeScript, Go, or Kotlin.
- Professional experience building both modern web front-end applications and backend web APIs, services, and integration layers.
- Demonstrated ownership of production application architecture across front-end, backend, data, and integration concerns.
- Experience planning and delivering work for a team, including breaking complex requirements into milestones and tasks and managing dependencies.
- Experience with structured release practices for distributed or multi-service systems, including versioning, change control, release coordination, and rollback.
- Strong understanding of API design, authentication and authorization patterns, data modeling, error handling, testing, and maintainability.
- Practical understanding of secure software development, including access control, data protection, dependency hygiene, and production readiness.
- Working knowledge of AI-first application patterns: tool calling, system instruction design, context management and retrieval grounding, and integrating LLM capabilities into a broader application architecture.
- Ability to onboard into a large, complex codebase quickly and become the technical authority with limited handoff.
- Strong communication skills, with the ability to explain technical tradeoffs, risk, and status to engineers, product stakeholders, and executive leadership.
Preferred Skills
- Experience with TypeScript, Node.js, and React.
- Experience with microservices, containerized applications, Kubernetes (AKS preferred), Helm, and CI/CD pipelines.
- Experience delivering in Azure Government, Azure GCC, or other compliance-constrained cloud environments.
- Experience with LangChain, LangGraph, Semantic Kernel, LlamaIndex, OpenAI/Azure OpenAI APIs, or similar frameworks.
- Experience serving as Release Manager or participating in a formal Change Control Board.
- Experience integrating with Microsoft 365, Teams, SharePoint, OneDrive, or similar enterprise collaboration platforms.
- Experience with OIDC/OAuth2, Keycloak, Entra ID, or other identity providers.
- Experience designing applications that handle sensitive data, enterprise permissions, auditability, or compliance requirements.
- Familiarity with DevSecOps practices, APM and observability tooling, autoscaling, and production support.
- Experience with AI-enabled document workflows, search and summarization, workflow automation, or agentic application patterns.
- Experience on SaaS, enterprise, government, or secure internal platforms.
WORKING CONDITIONS
Environmental Conditions
Work is performed in a standard office or remote work environment with minimal exposure to health or safety hazards.
Strength Demands
Sedentary: exerting up to 10 pounds of force occasionally. Work involves sitting most of the time.
Physical Requirements
Prolonged periods of sitting and working at a computer. Ability to communicate clearly in person, by phone, and through video conferencing, and to read and interpret documents and on-screen information.
Our Core Values
Employees of Agile Defense are our number one priority, and the importance we place on our culture here is fundamental. Our culture is alive and evolving, but it always stays true to its roots. Here, you are valued as a family member, and we believe that we can accomplish great things together. Agile Defense has been highly successful in the past few years due to our employees and the culture we create together.
What makes us Agile? We call it the 6Hs, the values that define our culture and guide everything we do. Together, these values infuse vibrancy, integrity, and a tireless work ethic into advancing the most important national security and critical civilian missions. It's how we show up every day. It's who we are.
- Happy - Be Infectious. Happiness multiplies and creates a positive and connected environment where motivation and satisfaction have an outsized effect on everything we do.
- Helpful - Be Supportive. Being helpful is the foundation of teamwork, resulting in a supportive atmosphere where collaboration flourishes, and collective success is celebrated.
- Honest - Be Trustworthy. Honesty serves as our compass, ensuring transparent communication and ethical conduct, essential to who we are and the complex domains we support.
- Humble - Be Grounded. Success is not achieved alone, humility ensures a culture of mutual respect, encouraging open communication, and a willingness to learn from one another and take on any task.
- Hungry - Be Eager. Our hunger for excellence drives an insatiable appetite for innovation and continuous improvement, propelling us forward in the face of new and unprecedented challenges.
- Hustle - Be Driven. Hustle is reflected in our relentless work ethic, where we are each committed to going above and beyond to advance the mission and achieve success.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $150K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Agile Defense, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $150K to $190K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Agile Defense AI Hiring
Agile Defense has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span McLean, VA, US, McLean, VA, Tampa, FL. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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).
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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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