Interested in this AI/ML Engineer role at Assured Information Security, Inc. (AIS)?
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About This Role
Assured Information Security (AIS) has an opening for a Senior AI Platform Engineer with our Mission Services team. In this role you will advise and support the design, integration, and maturation of Al\-enabling infrastructure across mission\-focused environments. This role is ideal for a technically strong engineer who understands how to stand up, assess, and guide modern Al platforms in secure government settings, with particular familiarity in cloud and enterprise ecosystems such as Microsoft Azure and Amazon Web Services (AWS).
\*This is an onsite position located in Columbia, MD and requires a TS/SCI w/CI Poly\*What You'll Do:
- Provide SETA support to government leadership and technical teams on Al platform architecture, integration, and sustainment.
- Advise on the design and evaluation of Al/ML platform environments across cloud, hybrid, and enterprise deployments.
- Support platform decisions involving Azure, AWS, Microsoft enterprise services, containerized environments, data pipelines, model hosting, and secure access patterns.
- Assess platform readiness for Al workloads, including compute, storage, networking, identity, security, observability, and scalability considerations.
- Help evaluate vendor offerings, reference architectures, technical tradeoffs, and implementation approaches.
- Support the development of technical documentation, architecture artifacts, white papers, decision briefings, and executive summaries.
- Coordinate with program managers, engineers, mission stakeholders, cybersecurity teams, and external partners to keep platform efforts aligned with mission outcomes.
- Identify integration risks, technical gaps, and dependencies, and recommend practical mitigation strategies.
- Assist with transition planning from prototype or pilot efforts into sustainable operational capability.
- Educate stakeholders on Al platform concepts, emerging patterns, and implementation best practices in government environments.
Education/Experience/Skillset Required:
- Bachelor’s degree in computer science, Engineering, Data Science, or related field with 12 years of relevant experience, or a Master’s degree with 10 years of relevant experience.
- Experience supporting Al/ML, data, cloud, or platform engineering efforts in government, defense, or enterprise environments.
- Demonstrated familiarity with Microsoft and/or AWS platform ecosystems.
- Working knowledge of cloud architecture, containers, APls, data services, identity/access management, and secure system integration.
- Ability to understand how Al applications depend on underlying platform components such as model hosting, pipelines, storage, orchestration, and monitoring.
- Ability to understand how Al applications depend on underlying platform components such as model hosting, pipelines, storage, orchestration, and monitoring.
- Builds, tests, and explores tools independently.
- Passionate about Al and platform modernization.
- Comfortable operating in ambiguous environments and helping teams find a path forward.
- Able to translate complex technical issues into actionable guidance for mixed technical and non\-technical audiences.
What Sets You Apart:
- Experience with Azure Al services, AWS Al/ML services, Kubernetes, Docker, MLOps, DevSecOps, Infrastructure as Code, or platform observability tools.
- Familiarity with secure government cloud environments and cross\-domain or restricted operational architectures.
- Understanding of Al governance, responsible Al, model lifecycle support, and platform security considerations.
- Experience supporting DoD, IC, or cyber mission organizations.
- Relevant certifications in cloud, systems engineering, cybersecurity, or platform operations.
Clearance Required: TS/SCI w/CI Poly
AA / Disability / Veteran
US Salary Range: $150,000 \- $165,000
The salary offered to a selected candidate will be based on several factors including location, skills, and experience. In addition to a comprehensive benefits package which includes employer paid health insurance and a 7% contribution to your 401k, candidates may be eligible for other forms of compensation.
AIS is a leading cyber and information security company supporting mission critical operations across the DoD, intelligence community, commerce and beyond. Founded in 2001, AIS is a privately\-owned company committed to excellence in all facets. We pride ourselves on offering some of the best benefits seen in the industry. We celebrate the unique backgrounds and experiences of our people and encourage new ideas, innovation, and the autonomy to create the future and career they want with our support.
\#CJ
Salary Context
This $150K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Assured Information Security, Inc. (AIS), 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($157K) sits 13% below the category median. Disclosed range: $150K to $165K.
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.
Assured Information Security, Inc. (AIS) AI Hiring
Assured Information Security, Inc. (AIS) has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Columbia, MD, US. Compensation range: $165K - $247K.
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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