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About This Role
AI Engineer, Senior
Full-time, APG, MD
Secret Clearance
Referral bonus: $1,500
Position Overview
CECOM ASIC is transforming how the U.S. Army develops, sustains, and modernizes mission platforms through the adoption of advanced artificial intelligence, automation, and data-driven capabilities. We are seeking a highly motivated AI Engineer to support the development and deployment of innovative mission-focused solutions.
In this role, you will work side-by-side with government personnel to design, build, and implement AI/ML models, agentic AI systems, analytics solutions, and automation workflows that directly contribute to Army readiness, decision advantage, and modernization initiatives.
This is a hands-on technical contributor position, not a management or team lead role. The selected candidate will provide advanced engineering expertise while supporting government personnel through collaboration, technical mentorship, and knowledge transfer to strengthen internal capabilities.
Key Responsibilities
- Develop and deploy mission-focused AI/ML models, analytics solutions, automation workflows, and agentic AI applications supporting Army and CECOM initiatives.
- Collaborate closely with government engineers, analysts, and stakeholders to deliver innovative and operationally relevant AI solutions.
- Design, develop, and maintain data pipelines, dashboards, Power BI solutions, retrieval-augmented generation (RAG) systems, and agent-based workflows.
- Build and implement Generative AI and Agentic AI solutions using frameworks such as LangChain, Semantic Kernel, AutoGen, Strands, CrewAI, LangGraph, and similar technologies.
- Integrate large language models (LLMs) with mission data sources, vector databases, memory systems, and orchestration frameworks while ensuring compliance with cybersecurity and governance requirements.
- Support the adoption of cloud-native AI practices, including MLOps, AIOps, CI/CD pipelines, model monitoring, and responsible AI implementation.
- Produce technical documentation, sustainment plans, and transition materials to support long-term government ownership and maintainability.
- Deliver technical coaching, knowledge transfer, and collaboration opportunities that help government personnel expand AI and data science competencies.
- Partner with mission stakeholders across organizations to ensure AI and analytics capabilities align with operational requirements and modernization objectives.
Required Qualifications
- Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, or a related technical discipline (or equivalent experience).
- Minimum of 4 years of experience developing and delivering AI/ML solutions.
- At least 1 year of experience working with Generative AI, Agentic AI, or multi-agent systems.
- Minimum of 2 years of hands-on Python development experience.
- At least 1 year of experience building agentic AI solutions using frameworks such as LangChain, Semantic Kernel, AutoGen, Strands, CrewAI, LangGraph, or similar platforms.
- Experience developing machine learning workflows, data pipelines, automation solutions, or advanced analytics capabilities.
- Experience supporting AI/ML workloads in Azure, AWS, or Google Cloud Platform (GCP).
- Demonstrated ability to translate mission or business requirements into technical solutions.
- Ability to obtain and maintain a U.S. Government security clearance.
Preferred Qualifications
- Experience supporting Department of Defense (DoD), U.S. Army, or Federal Government programs.
- Experience conducting technical training, mentoring, or knowledge transfer activities in government environments.
- Experience designing and implementing multi-agent systems for decision support, workflow automation, or mission operations.
- Knowledge of LLM prompt engineering, model tuning, secure RAG architectures, vector databases, and retrieval systems.
- Experience with MLOps, AIOps, DevSecOps, CI/CD pipelines, and cloud-native deployments.
- Experience integrating APIs, microservices, and event-driven architectures.
- Experience developing low-code business applications using Microsoft Power Platform, including Power Apps and Power Automate.
- Familiarity with Azure AI and Analytics services, including Azure OpenAI, Azure Machine Learning, Azure AI Foundry, Microsoft Fabric, Synapse Analytics, and related technologies.
About Semper Valens Solutions:
Semper Valens Solutions, Inc. (SVS) is a Service-Disabled Veteran Owned Small Business (SDVOSB) providing Cost Effective Software and Systems Engineering, Field Support, Training and Full Life cycle Support Management to the DOD and VA community.
At Semper Valens, our vision is to remain a creative, cutting edge and cost-effective solutions provider where our shared intellect, industry experience, and technology excellence, make a positive difference in our customer's success. Our solutions help bridge the gap between IT and business prioritizations to optimize budgets, risks and operational processes.
We search for outstanding technical professionals, hiring at all levels of the experience spectrum; intermediate, journeyman and senior. Consider us for your career plan.
Semper Valens Solutions is an Equal Opportunity Employer
Semper Valens Solutions proactively fulfills its role as an equal opportunity employer. We do not discriminate against any employee or applicant for employment because of race, color, sex, religion, age, sexual orientation, gender identity and expression, national origin, marital/parental status, pregnancy/childbirth, or related conditions, physical or mental disability, genetic information, status as a Disabled Veteran, Recently Separated Veteran, Active-Duty Wartime or Campaign Badge Veteran, Armed Forces Services Medal, or any other characteristic protected by law.
If you require a reasonable accommodation to apply for a position with Semper Valens Solutions through its online applicant system, please contact Semper Valens Solutions Human Resources Department at (830) 899-6870.
Semper Valens Solutions is an affirmative action/equal opportunity employer - minorities, females, disabled, and protected veterans are urged to apply. Applicants have rights under Federal Employment Laws.
All Jobs at Semper Valens Solutions: https://sempervalens.com/careers
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Semper Valens Solutions, 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 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Semper Valens Solutions AI Hiring
Semper Valens Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Belcamp, MD, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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