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About This Role
AI Engineer – Senior System Integrator Location: San Diego, CA
Company: 3 Reasons Consulting (3RC)
Customer: Naval Health Research Center (NHRC)
Employment Type: Full-Time On-site
Clearance: Active Secret Clearance required based on contract requirements About 3 Reasons Consulting 3 Reasons Consulting (3RC) is a growing government consulting and technology services firm supporting federal and Department of Defense customers with mission-critical technology, cybersecurity, artificial intelligence, data, and modernization initiatives. 3RC is seeking an AI Engineer / Senior System Integrator to support the Naval Health Research Center (NHRC) in San Diego, CA. This role will help integrate advanced AI capabilities into secure, enterprise environments supporting Navy research and mission applications. Position Overview The Senior AI Engineer – System Integrator will serve as a technical bridge between AI/ML development, infrastructure, software engineering, data engineering, cybersecurity, and mission stakeholders. The successful candidate will be responsible for integrating AI capabilities into operational environments and ensuring that AI/ML solutions can be securely deployed, monitored, maintained, and scaled. This is a hands-on engineering position for an experienced technologist who understands both AI/ML systems and the infrastructure required to operationalize them. Key Responsibilities AI/ML Systems Integration
- Design, integrate, deploy, and maintain AI/ML capabilities within enterprise and mission environments.
- Translate AI/ML requirements into scalable technical architectures and integration solutions.
- Integrate machine learning models, data pipelines, APIs, applications, and infrastructure into production environments.
- Support the transition of AI/ML prototypes and research efforts into reliable operational capabilities.
- Evaluate emerging AI technologies and recommend solutions aligned with mission and technical requirements.
- Troubleshoot complex integration issues across AI applications, infrastructure, data, networking, and security components.
AI Engineering & MLOps
- Support the development and implementation of MLOps pipelines for model development, testing, deployment, monitoring, and lifecycle management.
- Automate model deployment and operational workflows using modern DevSecOps practices.
- Implement version control, model versioning, automated testing, and reproducible deployment processes.
- Monitor model and system performance and support model lifecycle management.
- Integrate AI/ML workloads with containerized and cloud or hybrid infrastructure.
- Support deployment of AI workloads using Kubernetes and container technologies.
System Integration & Architecture
- Develop and maintain technical architectures supporting AI-enabled applications.
- Integrate AI services with existing enterprise systems, applications, databases, APIs, and infrastructure.
- Evaluate system dependencies, interfaces, data flows, and performance requirements.
- Develop and maintain system integration documentation, architecture diagrams, interface specifications, and technical procedures.
- Support system testing, integration testing, performance testing, and production deployment.
- Identify integration risks and develop mitigation strategies.
Data & AI Infrastructure
- Work with data engineers and AI/ML teams to support data ingestion, processing, transformation, and availability.
- Integrate AI workloads with structured and unstructured data sources.
- Support scalable compute, storage, networking, and GPU infrastructure required for AI workloads.
- Optimize AI/ML environments for performance, scalability, reliability, and resource utilization.
- Support data and model pipelines across development, test, and production environments.
DevSecOps & Automation
- Integrate AI/ML capabilities into secure CI/CD and DevSecOps pipelines.
- Automate infrastructure provisioning, configuration, testing, and deployment.
- Utilize Infrastructure as Code and configuration-management practices.
- Implement automated security, vulnerability, and compliance checks throughout the development lifecycle.
- Collaborate with DevSecOps engineers to establish repeatable and secure deployment processes.
Cybersecurity & DoD Compliance
- Ensure AI/ML systems and supporting infrastructure comply with applicable DoD cybersecurity requirements.
- Support Risk Management Framework (RMF), NIST 800-53, DISA STIG, and security authorization activities.
- Implement security controls around AI applications, models, APIs, containers, data, and infrastructure.
- Support vulnerability assessment and remediation activities.
- Maintain technical and security documentation required for authorization and operational support.
- Collaborate with cybersecurity teams, ISSOs, ISSMs, and system administrators to address security requirements.
Technical Leadership
- Serve as a senior technical advisor for AI system integration initiatives.
- Provide technical guidance to engineers, developers, data scientists, and infrastructure teams.
- Participate in architecture reviews, technical design sessions, and engineering working groups.
- Communicate complex AI and technical concepts to both technical and non-technical stakeholders.
- Help establish engineering standards, best practices, and repeatable processes for AI system integration.
Required Qualifications
- Security+ or other DoD 8570/8140-compliant certification.
- Active Secret clearance as required by the contract.
- 3+ years of experience in software engineering, systems integration, AI/ML engineering, or a related technical field.
- Demonstrated experience integrating complex software, data, and infrastructure components.
- Strong understanding of AI/ML concepts, model lifecycle management, and production AI systems.
- Experience with Python and modern software development practices.
- Experience with REST APIs, microservices, databases, and distributed systems.
- Experience with containers and Kubernetes or comparable orchestration platforms.
- Experience with CI/CD and DevSecOps methodologies.
- Strong Windows/Linux systems experience.
- Experience troubleshooting complex systems across application, infrastructure, data, and network layers.
- Excellent technical documentation and communication skills.
Preferred Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, Artificial Intelligence, Data Science, or related technical discipline.
- Experience supporting Department of Defense or Navy programs.
- Experience operationalizing generative AI, machine learning, computer vision, natural language processing, or other advanced AI capabilities.
- Experience with PyTorch, TensorFlow, scikit-learn, Hugging Face, or comparable AI/ML frameworks.
- Experience with LLMs, RAG architectures, vector databases, embeddings, and AI agents.
- Experience with GPU infrastructure and accelerated computing.
- Experience with MLflow, Kubeflow, Airflow, or comparable MLOps platforms.
- Experience with Docker, Kubernetes, Helm, GitLab, Jenkins, Argo CD, Terraform, or Ansible.
- Experience with PostgreSQL, MongoDB, Elasticsearch/OpenSearch, or vector databases.
- Familiarity with AI security, responsible AI, model governance, and AI/ML vulnerability management.
- Experience with NIST AI Risk Management Framework or comparable AI governance/security frameworks.
- Familiarity with RMF, NIST 800-53, DISA STIGs, and DoD 8140/8570 requirements.
- Security+ or other DoD-compliant cybersecurity certification.
What Success Looks Like The successful AI Engineer – Senior System Integrator will help NHRC move AI capabilities beyond experimentation and into secure, scalable, operational environments. Success will include reliable integration of AI/ML applications with enterprise infrastructure, streamlined deployment and MLOps processes, effective collaboration across engineering disciplines, and AI solutions that meet the performance, security, and operational requirements of a DoD environment. This is an excellent opportunity for a senior engineer who wants to work at the intersection of AI, software engineering, systems integration, and national security while supporting meaningful Navy research and technology initiatives. Why Join 3RC?
- Support a mission-focused Navy customer at the Naval Health Research Center.
- Work directly with emerging AI/ML and MLOps technologies.
- Help operationalize AI capabilities in a challenging DoD environment.
- Collaborate with AI engineers, data scientists, software developers, cybersecurity professionals, and infrastructure engineers.
- Join a growing government consulting firm where technical expertise has a direct mission impact.
Salary Range: $120,000 - $135,000 Benefits at 3 Reasons Consulting
At 3 Reasons Consulting, we are committed to supporting the well-being of our team with a comprehensive benefits package that includes both company-paid and shared-cost options. Our benefits are designed to enhance your health, financial security, and work-life balance to help you thrive personally and professionally as a valued member of our team.
Company-Paid Benefits
- Short/Long Term Disability
- Basic Life Insurance
- Direct Payroll Deposit
- Leave Accrual
- Holidays
- 401(k) Match
Employee / Company Shared Benefits
- Additional (Voluntary) Life Insurance
- 401(k)
- Medical Coverage
- Dental Coverage
- Vision Care Plan
- Flexible Spending Account Plan
3 Reasons Consulting is an Equal Opportunity Employer. We are committed to providing a workplace free from discrimination or harassment and hold all 3 Reasons employees accountable to protect this mission. We do not discriminate on the basis of race, color, gender, religion, national origin, sexual orientation, age, marital status, veteran status, military status, disability status, or any other characteristic protected by federal, state, or local law. All applicants will receive consideration for employment without regard to protected bases.
Salary Context
This $120K-$135K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At 3 Reasons Consulting, LLC, 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. This role's midpoint ($127K) sits 40% below the category median. Disclosed range: $120K to $135K.
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.
3 Reasons Consulting, LLC AI Hiring
3 Reasons Consulting, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US. Compensation range: $135K - $135K.
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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