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About This Role
Description:
Sonalysts, Inc. is seeking a highly skilled Artificial Intelligence & Machine Learning Systems Engineer with deep expertise in
Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Generative AI, AI agents, and Model Context Protocol.
The ideal candidate should have experience in designing, developing, and optimizing AI-powered applications and workflows with a strong foundation in cybersecurity best practices.
Who are we?
Sonalysts, Inc. is a small, 100% employee-owned business (ESOP) that supports a very diverse set of customers. While the company was originally founded in 1973 with a focus on sonar analysis for the Navy (hence the name), Sonalysts has since expanded into a variety of other Defense and Commercial spaces. With current contracts supporting the Navy, Air Force, Army, and many other customers, Sonalysts leverages the skills and knowledge of its employee owners to help solve the Department of Defense’s most challenging problems while providing the best benefit to our customers. Sonalysts has a diverse and capable workforce comprised of former and retired service members from all branches of the military, and professionals with a wide range of commercial and technical backgrounds. From software development to operations analysis, doctrinal development, engineering, wargaming, targeted knowledge elicitation, or large group facilitation, Sonalysts can meet any challenge.
A small, central corporate structure coupled with a largely de-centralized approach to project management enables Sonalysts to compete with larger Defense Contractors in providing rapid, innovative solutions to meet customer needs while remaining agile and entrepreneurial at heart. Our employee-owners lead and support existing projects while also developing new business and winning new work.
If you are looking to use your technical knowledge to solve challenging problems for the military while benefiting from the flexibility and empowerment of being a contractor or potential future leader in a small business, then Sonalysts may be the place for you.
Key Responsibilities:
- Design and implement scalable LLM-based architectures for AI-powered applications.
- Develop and optimize Retrieval-Augmented Generation (RAG) pipelines for improved model accuracy.
- Work with Generative AI models to enhance automated decision-making systems.
- Integrate and deploy AI models using AI Agents, Model Context Protocol, and retrieval strategies.
- Develop automation workflows using N8N, Pydantic, or other AI workflow tools.
- Optimize and fine-tune AI/ML models for performance and efficiency.
- Utilize GPU acceleration and frameworks like TensorFlow, PyTorch for large-scale AI workloads.
- Implement secure coding practices and cybersecurity protocols in all AI/ML development workflows.
- Ensure secure data handling, model access control, and compliance with data privacy regulations.
- Collaborate with cross-functional teams to integrate AI solutions into production environments.
- Research and implement state-of-the-art advancements in LLMs, multimodal AI, and generative systems.
Required Qualifications & Experience:
- Must be a U.S. citizen, eligible for a U.S. Department of Defense (DoD) SECRET security clearance*
- Bachelor's degree in Computer Science, AI, Machine Learning, Cybersecurity, or a related field.
- 3 or more years of professional experience that includes developing and integrating LLMs, RAG, and Generative AI based solutions.
- Strong problem-solving skills and ability to work in fast-paced, AI-driven environments.
Desired Experience:
- US Navy Combat Systems: Proven experience working with US Navy combat systems, specifically applying systems engineering and software development principles to mission-critical defense environments.
- Containerization & Orchestration: Deep technical proficiency in software containerization technologies, including Docker, Kubernetes, and OpenShift, for the deployment and scaling of complex software architectures.
- Experience with vector databases, knowledge retrieval strategies, and fine-tuning AI models.
- Knowledge of cloud-based AI deployment (AWS, GCP, Azure) with secure infrastructure practices.
- Familiarity with open-source AI tools and frameworks for custom AI solution development.
- Knowledge of compliance standards such as DoD Risk Management Framework (RMF), NIST, and ISO/IEC 27000 series.
- Contributions to AI research, publications, or open-source projects are a plus.
- Strong background in Python, AI frameworks (TensorFlow, PyTorch), and GPU architectures for model training/inference.
- Experience with AI Agent frameworks and Model Context Protocol for scalable AI solutions.
- Hands-on experience with N8N, Pydantic, or other AI workflow tools.
- Demonstrated experience implementing cybersecurity controls in AI environments (e.g., secure APIs, authentication/authorization, encryption).
- Familiarity with threat modeling, vulnerability assessment, and secure deployment of AI models.
- Deep understanding of retrieval systems, knowledge graphs, and multimodal AI applications.
What’s in it for you?
- Opportunity to provide important support to the DoD and to expand your work portfolio to support various projects across the company.
- Opportunity to grow your skillset and pursue project leadership.
- Salary ranges between $85,000-$130,000, dependent on experience, qualifications and other relevant business criteria.
- Becoming an integral part of an innovative employee-owned company which includes:
- + Flexible Time Program enabling an optimal work-life balance by permitting partners to arrange their work around personal appointments and vacations, parent-teacher conferences, children’s activities and the like, so long as the partner gets the job done on time. + Paid Time Off + 401(k) Plan with company match + Employee Stock Ownership Plan (ESOP) + Sales Bonus Program + Special Performance and Retention Bonus Program + Health and Preventive Dental Insurance + Dependent Care Assistance Plan + Health Care Reimbursement Plan + Employee Referral Bonus Program + Professional Development (e.g. Tuition Reimbursement Program, Online Training Program, and Targeted Skills Program) + Relocation Assistance
- Obtaining a U.S. Government security clearance involves a comprehensive background check. Candidates are eligible for a clearance if they have demonstrated sound financial management (including good credit) over time, are free of criminal records, have limited foreign contacts or ties, and other factors indicative of a position of trust to protect information sensitive to the U.S. Government.
Sonalysts, Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to, and will not be discriminated against on the basis of, race, religion, color, national origin, sex, sexual orientation, gender identity, age, status as a protected veteran, disability, or other basis protected by law.
This job is not available to residents of New York City because of local law restrictions there.
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www.sonalysts.com
Salary Context
This $85K-$130K 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 Sonalysts, 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 $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 ($107K) sits 49% below the category median. Disclosed range: $85K to $130K.
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.
Sonalysts, Inc. AI Hiring
Sonalysts, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Waterford, CT, US. Compensation range: $130K - $130K.
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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