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About This Role
About Day \& Zimmermann
No matter the mission, SOC delivers! We provide a full suite of integrated solutions that support security and protection needs in high\-threat environments across the globe. Our reputation as a responsive, agile and trusted partner precedes us. Still not convinced? We’re trusted by the U.S. Government and other commercial clients to detect and deter the full spectrum of threats in support of national security interests. As a Day \& Zimmermann company, we have the strength to achieve mission objectives in any corner of the world. Come join in on our purpose – We put people to work, we protect American freedoms, and we help our customers power and improve the world! https://www.soc\-usa.com/about\-us
Job Summary
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We’re looking for a full\-time, staff Sr. AI Solutions Developer to join our SOC team. The Sr. AI Developer, GS role will lead the design, development, and deployment of AI\-driven solutions that streamline high\-effort processes, improve forecasting accuracy, and enhance market and client intelligence. This position is a strategic opportunity to pioneer how advanced analytics, and generative AI can reshape critical workflows in architecture, engineering, and security services.
Responsibilities
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- Build and deploy custom generative AI tools for proposal generation, document processing, and engineering automation. Design predictive analytics models for market intelligence, client behavior, and bid strategy optimization. 40%
- Lead the development of financial analysis \& forecasting tools to enhance revenue projections and control cost, cash flow modeling, and budgeting accuracy. 30%
- Proactively manage data security risks and maintain safe, explainable, and bias\-mitigated AI systems. Ensure full compliance with government standards (NIST, DoD, CMMC, FedRAMP) and implement ethical AI governance. 5%
- Support executive leadership with data\-powered insights that inform strategic planning and resource allocation. 5%
- Partner with teams across engineering, finance, operations, and business development to turn challenges into AI\-driven solutions. 20%
KSAs (Knowledge, Skills, and Abilities)
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- Deeply familiar with large language models (LLMs) including OpenAI’s GPT series, Anthropic Claude, Cohere, Gemini and open\-source models like LLaMA or Mistral; capable of fine\-tuning, prompt engineering, evaluation, and safety alignment for enterprise and government use cases.
- Experienced in Agentic AI development, including the design and orchestration of multi\-step, goal\-directed AI agents that interface with APIs, databases, and user workflows. You understand frameworks such as LangChain, OpenAgents, AutoGPT, and are equipped to build autonomous or semi\-autonomous agents that interact contextually and responsibly.
- Skilled in MLOps practices, including CI/CD, model lifecycle management, and deployment orchestration with tools like MLflow, Airflow, and Kubeflow.
- Experienced with cloud\-based AI platforms (e.g., AWS SageMaker, Azure ML, Google Vertex AI) and integrating models within enterprise\-grade data ecosystems.
- Adept at secure AI development compliant with CMMC, FedRAMP, NIST and related standards, with a strong awareness of data privacy, model governance, and sensitive information protection.
- Comfortable working with unstructured data, embedding models, vector search engines (e.g. FAISS, Pinecone), and building Retrieval\-Augmented Generation (RAG) pipelines.
Minimum Qualifications
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- Bachelor’s Degree in Computer Science, Engineering, Mathematics, Statistics required
- Master’s degree preferred
- 5\+ years Experience with Python and machine learning libraries such as TensorFlow, PyTorch, Scikit\-learn, and transformer\-based frameworks (e.g., Hugging Face) required.
- 7\+ years Professional experience preferred.
- Must be able to obtain a U.S. security clearance.
- Great attitude and team player.
- Successful completion of background and drug screening process.
Compensation and Benefits
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In compliance with this state’s pay transparency laws, the salary range for this role is $116,080\.00 \- $188,630\.00\. This is not a guarantee of compensation or salary, as final offer amount may vary based on factors including but not limited to experience and geographic location. (The specific programs and options available to an employee may vary depending on date of hire, schedule type, and the applicability of collective bargaining agreements).
We care about our employees and it shows. Our staff receive a competitive salary and a comprehensive benefits package which includes medical/Rx, dental and vision coverage; life, AD\&D and disability insurance; flexible spending accounts; 100% paid maternity leave for up to 12 weeks, parental leave, family leave, other paid time off; voluntary benefits and discount programs to meet our employees’ individual needs including pet insurance for our furry family members!
Essential Functions
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To ensure a safe work environment while meeting the physical demands of the job, you must be able to perform the following physical and mental tasks, with or without a reasonable accommodation:
- Visual acuity (e.g., needed to prepare and analyze data, to transcribe documents, to view a computer, to read, to inspect objects, to operate machinery).
- Manual Dexterity (e.g., picking, pinching, typing, or other working that uses the fingers).
- Hearing.
- Talking.
- Capacity to think, concentrate and focus over long periods of time.
- Ability to write complex documents in the \[English] language.
- Ability to read complex documents in \[English] language.
- Capacity to express thoughts orally (e.g., accurately, quick and loudly convey spoken instructions to workers).
- Capacity to reason and make sound decisions.
- Ability to regularly perform all job functions at Company’s office or work site.
Diversity, Inclusion \& Equal Employment Opportunity
SOC, a Day \& Zimmermann company, is an Equal Opportunity Employer, M/F/D/V. Diversity, and Inclusion \& Equal Employment Opportunity: Day \& Zimmermann is committed to maintaining an inclusive workforce, where employees are hired, retained, compensated and promoted based on their contributions to our Company. Our collective strength is rooted in over 110 years of diverse employees and businesses, commitment to success, and delivery on promises made. Federal and state Equal Employment Opportunity laws prohibit employment discrimination based on race, color, religion, sex, sexual orientation and gender identity, age, national origin, citizenship status, veteran status and disability status. Day \& Zimmermann is committed to providing an equal opportunity work environment in full compliance with these laws. If you are an individual with a disability and you require an accommodation in the application process, please email reasonableaccommodation@dayzim.com.
\#dzsoc
Salary Context
This $116K-$188K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Day & Zimmermann, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($152K) sits 9% below the category median. Disclosed range: $116K to $188K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Day & Zimmermann AI Hiring
Day & Zimmermann has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span VA, US, Philadelphia, PA, US. Compensation range: $120K - $188K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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