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About This Role
Req Id: 458 Location: CLARKSBURG, MD, US, 20871
Date: Oct 12, 2026 Thales Defense & Security, Inc. (TDSI) Headquartered in Clarksburg, MD, is a U.S. company that has been serving domestic and international defense Air, Land and Sea domains for 60+ years. With over 1,000 employees, TDSI and its four subsidiaries (Advanced Acoustic Concepts, Digital Receiver Technology, Tampa Microwave, and Trusted Cyber Technologies), supports U.S. Joint Services, special operations forces and multi-national partners with a variety of communication, visualization, signal intelligence/electronic warfare, and sensor/sonar capabilities. TDSI’s key technologies include tactical handheld radios, expeditionary satellite terminals, helmet-mounted displays, optics and target recognition capability, advanced radars, undersea sensing and missile systems. Lives depend on what we do!
Top Benefits (Subject to terms of applicable policies/plans)
- Competitive Salary
- Yearly Incentive Compensation Plan
- Medical insurance for self and eligible family members
- Generous 401(k) program- up to 7% company contribution and 100% immediate vesting
- Tuition Reimbursement
- Paid wellness, vacation, and holiday leave
- Paid maternity, paternity, and parental leave
- 9/80 Work Schedule (every other Friday off)
- Hybrid working environment
We are seeking a hands-on Cloud Security Engineer with practical experience in Azure and/or AWS who can help build and operate core cloud security controls while integrating security into AI/ML workflows. This foundational role will partner across teams to deliver measurable improvements in identity, network segmentation, data protection, logging/monitoring, and compliance for DoD and government workloads. This role is located in our Clarksburg, MD office and will report to the Director of Cybersecurity.
Responsibilities
- Establish baseline cloud security controls (IAM/Entra ID, network segmentation, encryption, logging) with dashboards and runbooks.
- Deploy or tune CSPM/CWPP tooling; reduce misconfigurations and critical findings by agreed targets.
- Deliver audit-ready evidence for FedRAMP/CMMC/NIST SP 800-53 control families in scope.
- Stand up detections for cloud and AI-specific threats in Microsoft Sentinel or equivalent SIEM; document response playbooks.
- Design and operate security controls across Azure/AWS: IAM/Entra ID, VNet/VPC controls, NSGs/Security Groups, WAF, private endpoints/PrivateLink.
- Implement Zero Trust/least privilege patterns; establish guardrails. (Azure Policy, AWS SCPs, OPA/Gatekeeper).
- Deploy and maintain CSPM/CWPP tooling and integrate with SIEM (Microsoft Sentinel / Splunk).
- Protect AI/ML pipelines pragmatically: secure training data, model repos, and inference endpoints; apply access controls and logging.
- Conduct threat modeling and risk assessments; document findings with prioritized remediation plans and partner follow-through.
- Support compliance for FedRAMP, CMMC, NIST SP 800-53; produce artifacts and evidence for audits.
- Maintain runbooks, diagrams, and SOPs; drive continuous improvement through metrics.
Qualifications
The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- U.S. Citizenship required. - Applicants selected may be subject to a government security investigation and must meet eligibility requirements for access to classified information.
- Bachelor’s degree in a technical field or equivalent experience.
- 3 - 6 years in cybersecurity with 2+ years focused on Azure and/or AWS security in enterprise or government environments.
- Hands-on experience with IAM, network security, encryption, logging/monitoring, and CSPM/CWPP tools.
- Experience with infrastructure-as-code and policy-as-code; scripting for automation (PowerShell/Python/Bash).
- Working knowledge of compliance frameworks (FedRAMP, CMMC, NIST SP 800-53, DoD Cloud SRG).
- Strong communication and collaboration skills; ability to document and drive cross-team remediation.
- Ability to participate in a rotating on‑call schedule to support continuous service and respond to priority issues as needed
Preferred
- Certifications: CISSP, CCSP, CISM, Azure Security Engineer, Security+.
- Experience integrating security into MLOps, LLMs, or generative AI applications.
- Container/Kubernetes security experience; service mesh and runtime protections.
- Experience with Azure Stack Edge family of devices.
- Experience with DoD networks, especially classified and operational networks.
- Understanding of enterprise networking concepts, especially Virtual Private Networks (VPNs), tunneling, QoS, and cloud connectivity solutions (e.g. Azure Express Route).
The annualized pay range for this role is $88,920.00 - $120,000.00 USD along with a target incentive compensation plan (ICP) of 5%. The pay range provided is a good faith estimate representative of the experience level for the role described above. TDSI considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate’s work experience, location, education/training, and key skills.
For specific questions about this job posting, candidates may contact talent acquisition at [email protected].
Thales Defense & Security, Inc. is committed to non-discrimination as applicable under federal, state and local laws. We are an E-Verify Employer.
If you need an accommodation or assistance in order to apply for a position with Thales Defense & Security, Inc., please call Human Resources at 240-864-7000.
Nearest Major Market: Washington DC
Salary Context
This $88K-$120K range is in the lower quartile 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 Thales, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($104K) sits 51% below the category median. Disclosed range: $88K to $120K.
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.
Thales AI Hiring
Thales has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Orlando, FL, US, Clarksburg, MD, US, Austin, TX, US. Compensation range: $120K - $252K.
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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