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About This Role
Senior Platform Engineer (AI Applications)
Improvix Technologies | Federal Program | Fully Remote
Location: Fully Remote (U.S.)
Clearance: Active Secret clearance required.
Employment Type: Full-Time
THE ARCHITECTURE DIAGRAM DOESN’T DEPLOY ITSELF
Every data and AI program reaches the same moment. The architecture is approved, the model works in a notebook, and the integration pattern looks clean on a whiteboard. Then someone has to build the service, wire it into ServiceNow and the identity provider, get it through the pipeline and the security scan, and keep it running when an upstream API changes its response format overnight. That person is usually the difference between a capability that ships and one that stays a slide.
Improvix Technologies is hiring a Senior Platform Engineer to build and own the application services, APIs, and enterprise integrations that turn a federal data and AI program into working capability. You’ll take capabilities from design through production — through the security pipeline and the authorization process — and you’ll own what you build after it goes live.
This is a hands-on software engineering role, and it’s fully remote. The team already has deep AI and data expertise in an AI Solutions Architect, platform and agent infrastructure depth in a Senior Agentic Platform Engineer, and a Technical Delivery & Integration Lead who clears the path across partner teams. What it needs is the engineer who builds and connects the services between them. If your strength is infrastructure alone, or models alone, this will be a harder fit than the title suggests.
WHY ENGINEERS TAKE THIS ROLE
- Genuinely remote, genuinely cleared. A cleared federal data and AI role you can do from anywhere in the U.S. is genuinely rare. That’s not a perk we bolted on — it’s how the program runs.
- You own the capability, not a ticket. You take a capability from approved design to production and stay its named owner after launch — no handoff to an ops team that has to learn it from scratch.
- You’re the one who connects it all. AI services, enterprise platforms, identity, and data sources meet in the code you write. On this team, the integrations are the product, not an afterthought.
- Applied AI that reaches real users. You’ll put LLM-backed services and retrieval features into production inside a real authorization boundary, not a sandbox that never leaves the demo.
- Specialists on either side of you. An AI architect, an agentic platform specialist, and a delivery lead who handles cross-team coordination — so your time goes to building, not chasing approvals or reinventing the model layer.
WHAT YOU’LL OWN
Application Services & APIs
- Design, build, and maintain backend services and APIs in Python, Java, Go, or TypeScript that put data and AI capabilities in front of users and systems.
- Build front-end components or internal tooling where a capability needs them.
- Hold the line on code quality through reviews, automated testing, and shared engineering standards.
- Design services for failure: timeouts, retries, idempotency, and clear behavior when a dependency is down.
Enterprise Integrations & Identity
- Build and maintain integrations with ServiceNow, enterprise data platforms, and internal APIs — REST, messaging, and event-driven patterns, with real error handling and monitoring.
- Integrate services with enterprise identity providers using OAuth 2.0, OIDC, and SAML, including service-to-service authentication and least-privilege authorization.
- Design integrations that fail loudly rather than quietly, so a broken sync is caught in minutes rather than discovered by a user weeks later.
- Give the Technical Delivery & Integration Lead the technical detail behind each cross-team request, and execute the engineering work once dependencies clear.
DevSecOps & Authorization
- Build and own CI/CD pipelines with automated build, test, security scanning, and deployment.
- Embed security across the lifecycle: SAST and DAST, dependency and container scanning, secrets management, and least-privilege access.
- Own the ATO work for your capabilities — control implementation evidence, STIG or equivalent hardening, and remediation of findings — so authorization isn’t a scramble at the end.
Applied AI
- Turn designs from the AI Solutions Architect into production features: LLM-backed services, retrieval-augmented generation (RAG), and model API integrations.
- Build the application layer around models — prompt and configuration management, evaluation hooks, guardrail enforcement, and usage and cost tracking.
- Monitor AI features in production for quality, latency, cost, and safety, and tune them as usage grows.
Cloud & Runtime
- Deploy and run your services in AWS or Azure, including their government regions, using infrastructure as code and containers on Kubernetes or managed equivalents.
- Partner with the Senior Agentic Platform Engineer on the shared cloud, model-hosting, and agent infrastructure your services run on.
- Build observability in from the start: logging, metrics, tracing, alerting, and dashboards someone actually looks at.
Capability Ownership
- Break capabilities into deliverable increments, estimate honestly, and surface risks and dependencies while there’s still time to act on them.
- Write the runbooks, technical documentation, and handoff material that let someone else support what you built.
- Lead post-incident fixes for the capabilities you own — the fix that prevents the next incident, not just the one that closes the ticket.
WHAT YOU BRING
These are the requirements — everything in “Bonus Points” below is genuinely optional. If you meet this list, apply.
- 8+ years of software engineering experience, including 3+ years building and owning production services end to end.
- Strong programming ability in at least one backend language — Python, Java, Go, or TypeScript — and a track record designing RESTful APIs and services other systems depend on.
- Hands-on experience integrating with enterprise platforms — ServiceNow or a comparable system — through APIs, messaging, or event streaming.
- Identity integration experience: OAuth 2.0 and OIDC (or SAML), working with an enterprise identity provider such as Entra ID, Okta, or Keycloak.
- Experience building and maintaining CI/CD pipelines — GitLab CI, GitHub Actions, Azure DevOps, or Jenkins — with integrated security scanning.
- Hands-on ATO or equivalent compliance work: producing control evidence, hardening systems to a baseline, and remediating findings.
- At least one AI or ML capability delivered to production — an LLM-backed service, a RAG feature, or a model integration — not a notebook and a demo.
- Working experience deploying to AWS or Azure with infrastructure as code (Terraform, CloudFormation, or Bicep) and containers (Docker and Kubernetes or a managed equivalent).
- Experience building observability into systems and supporting them in production, including incident response.
- The ability to work independently on a remote team and explain a technical tradeoff to engineers and non-technical stakeholders alike.
- Bachelor’s degree in a technical field, or an equivalent combination of education and directly relevant experience.
- U.S. citizenship and an active Secret clearance.
- Availability during East Coast business hours to stay in sync with the program.
BONUS POINTS
- ServiceNow-specific integration depth — IntegrationHub, MID Server, Scripted REST APIs, or Flow Designer.
- Hands-on deployment in AWS GovCloud or Azure Government.
- Familiarity with FedRAMP, NIST 800-53, and RMF.
- Experience with model hosting services such as Amazon Bedrock or Azure OpenAI, vector databases, and LLM frameworks.
- LLM evaluation or guardrail tooling experience.
- Experience building tools or APIs that AI agents call, including tool-calling or Model Context Protocol (MCP) integrations.
- Certifications — CompTIA Security+ or another DoD 8140 baseline, AWS or Azure developer or professional-level, or Certified Kubernetes Application Developer (CKAD).
- Prior experience supporting federal or defense customers in a cleared environment.
WORKING HERE
Improvix Technologies delivers secure, modern, integrated technology solutions that keep the nation's most critical systems running. We're small enough that a good idea reaches a decision-maker in a day, and close enough to the mission that you'll know exactly what your work supports.
On remote work: this role is fully remote within the U.S., and we mean it — no quiet expectation that you'll relocate to DC in six months, and no “remote for now.” You'll need to be reachable during East Coast business hours to stay in sync with the program, and that's the extent of it.
What “own” means here: when a capability you built has a problem in production, you’re the first to know and the person who fixes it. We back that with a team that shares the load and runbooks that make it manageable — but we’re hiring someone who wants their name on a production system, not someone who’d rather hand it off at launch.
A straight answer on breadth: this role spans services, integrations, security, AI features, and the cloud they run on, and nobody arrives expert in all of them. We expect real depth in building and integrating software and the willingness to get competent in the rest quickly. If you’d rather go deep on infrastructure or on models alone, there are specialists on this team doing exactly that — and we’d rather say so now.
Ready to apply? Send us your résumé. If there’s a system you took from design to production and kept running, an integration you made reliable after it kept breaking, or a service you got through an ATO, tell us about it — that’s the conversation we want to have.
WHY IMPROVIX
At Improvix Technologies, we don't just deliver IT solutions — we deliver mission success. Our teams support critical government programs by building secure, innovative, and scalable technology that helps agencies operate more efficiently and securely.
We believe great technology starts with great people, so we invest in our employees' growth, well-being, and long-term success. Our benefits include:
- Competitive compensation and performance-based incentives
- 401(k) with company match
- Three weeks of paid annual leave that increases with tenure
- Comprehensive medical, dental, and vision insurance
- Life insurance and disability coverage
- Transit and commuting benefits
- Professional development and paid training opportunities
EQUAL OPPORTUNITY EMPLOYER
Improvix Technologies, Inc. is an Equal Opportunity Employer and is committed to building a diverse and inclusive workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, or any other characteristic protected by federal, state, or local law.
Improvix is committed to providing reasonable accommodations to qualified individuals with disabilities during the application and employment process. If you require assistance or accommodation, please contact our Human Resources team.
Security Clearance / Citizenship Requirements
Due to the nature of our government contracts, U.S. citizenship may be required for certain positions, and some roles may require the ability to obtain and maintain a U.S. government security clearance.
Pay Transparency Statement
In accordance with applicable laws and regulations, Improvix Technologies will not discharge or otherwise discriminate against employees or applicants for discussing, disclosing, or inquiring about compensation.
Salary Context
This $180K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Improvix Technologies, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($200K) sits 6% below the category median. Disclosed range: $180K to $220K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Improvix Technologies AI Hiring
Improvix Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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