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About This Role
US Based Position. Must be US Citizen.
Location: Arlington VA(U.S.-based). Remote Work Allowed in US. Employment Type: Full-Time Company: Technology & Business Management, Inc. (TBM Inc.)
Experience Level
8+ years in security analytics/SIEM engineering with 4+ years of advanced Splunk engineering; hands-on dashboarding, SPL, alerting, data onboarding, and automation required.
Position Summary
Own DLP telemetry, analytics, dashboards, alerting, automation, and measurable optimization in Splunk, while supporting Government-approved SOAR/RPA and bounded AI/ML use cases.
Key Responsibilities
Engineer and maintain Splunk ingestion, normalization, searches, dashboards, reports, alerts, health metrics, event-volume trends, and operational/executive DLP reporting.
Develop SPL queries and documented calculations that Government personnel can reproduce and sustain.
Integrate telemetry from Symantec/Broadcom, Purview, Palo Alto, and other authorized DLP/security platforms.
Design automated notifications, alerts, alarms, and Government-authorized SOAR/RPA workflows to reduce manual triage and improve response.
Support event correlation, incident analytics, severity/prioritization, trend analysis, and detection-performance measurement.
Evaluate approved AI/ML-enabled capabilities to reduce false positives, identify notable events, improve triage, and reduce analyst workload; establish baseline/candidate comparisons and rollback criteria.
Track metrics such as false positives, false negatives where measurable, alert volume, time-to-triage/disposition, stability, workload, and business impact.
Document data definitions, dashboard maintenance, automation logic, model/configuration tuning, test evidence, limitations, procedures, and Government training.
Required / Critical Skills
Splunk Enterprise / Splunk ES; SPL; dashboards; data models; alerts; field extraction; ingestion/onboarding; CIM; APIs; security analytics; incident correlation.
Splunk SOAR or comparable orchestration/automation; Python or scripting; REST APIs; JSON; data normalization.
Understanding of DLP events/policies, SOC workflows, detection engineering, false-positive reduction, and security KPIs.
Practical AI/ML analytics knowledge with emphasis on explainability, human review, validation, privacy/security controls, and measurable benefit rather than custom model research.
Splunk Core Certified Power User/Admin/Architect or Splunk Enterprise Security certification strongly preferred.
Preferred Qualifications
Experience with Qmulos, federal continuous monitoring/FISMA reporting, or large federal Splunk environments.
Experience integrating DLP products into SIEM/SOAR workflows.
Current Public Trust/MBI or clearance.
Education
Bachelor’s degree in Cybersecurity, Information Technology, Computer Science, Engineering, Information Systems, or a related field is preferred. Equivalent directly relevant experience and advanced industry certifications may be considered, subject to the applicable contract labor-category requirements.
Federal Suitability / Security
Candidate must be able to meet IRS personnel-security and suitability requirements for the position, including the applicable background investigation and required security/privacy training. A current favorably adjudicated federal Public Trust/MBI or other investigation that may qualify for reciprocity is highly desirable.
What Will Make a Candidate Stand Out
Direct hands-on experience with the named platform(s), not only governance or oversight.
Recent enterprise production experience supporting sensitive or regulated information.
Ability to explain specific examples of troubleshooting, policy/rule tuning, testing, incident support, measurable improvement, and documentation.
Federal customer experience and demonstrated ability to work within controlled access, change, audit, and documentation processes.
Ability to become productive quickly and communicate effectively with both engineers and Government stakeholders.
Why Join TBM Inc.
- Support high-impact federal missions
- Work on strategic IT modernization programs
- Join a fast-growing federal consulting firm
- Collaborate with experienced government and industry leaders
- Opportunity to grow into senior consulting and portfolio management roles
How to Apply
Interested candidates should apply via Indeed with their resume. Qualified applicants will be contacted for next steps.
TBM Inc. is an equal opportunity employer. All qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, disability, or veteran status.
Job Type: Full-time
Pay: $145,000.00 - $155,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Employee assistance program
- Employee discount
- Flexible schedule
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Professional development assistance
- Referral program
- Retirement plan
- Tuition reimbursement
- Vision insurance
Work Location: Remote
Salary Context
This $145K-$155K 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 Technology & Bussiness Managment Inc., 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($150K) sits 29% below the category median. Disclosed range: $145K to $155K.
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.
Technology & Bussiness Managment Inc. AI Hiring
Technology & Bussiness Managment Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $155K - $155K.
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/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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