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About This Role
Unissant, Inc. delivers innovative capabilities to the agencies that keep our nation healthy and safe. We apply our domain expertise, data acumen, and technology know-how to achieve breakthrough results for our clients. Working collaboratively, we advance missions and careers through a focus on honesty, integrity, and dependability. We continuously look for talent, excited to join that effort. To learn more about our exciting organization, please visit us at www.unissant.com.
We are seeking an AI Solutions Engineer - Agent Development to join our MissionWorks team and build AI-enabled capabilities for customer and internal use cases.
The ideal candidate is a hands-on builder with strong software-development fundamentals, intellectual curiosity, and a practical interest in turning ideas into useful AI capabilities. This individual will work with the MissionWorks Solutions Executive and other technical team members to turn defined mission needs into AI agents, prototypes, workflow automations, integrations, and reusable software components.
This role is for an engineer who wants to build before every requirement is fully defined. The successful candidate will be comfortable learning quickly, working through ambiguity, testing ideas with users, and improving solutions based on what works in a real operating environment. They will contribute to prototypes and pilots that demonstrate value quickly and can mature into secure, operational capabilities.
Essential Duties and Responsibilities:
- Develop and maintain AI agents, AI-enabled prototypes, workflow automations, software components, integrations, and proof-of-concepts for MissionWorks initiatives.
- Use Python to develop, test, debug, and maintain AI-enabled software, services, integrations, and technical components; apply C++ where required by the solution environment or performance needs.
- Assist with the development of AI-enabled applications using approved tools, platforms, APIs, models, and enterprise data sources.
- Design and refine agent workflows, prompts, tool use, retrieval approaches, human-review steps, and output-evaluation methods.
- Support the integration of applications with APIs, structured and unstructured data sources, cloud services, knowledge repositories, and internal business systems.
- Work with senior technical staff to translate user needs and defined use cases into technical tasks, prototypes, experiments, and iterative releases.
- Participate in solution design discussions, technical working sessions, customer demonstrations, testing activities, and user feedback sessions.
- Build, test, debug, document, and refine software solutions in accordance with established development, security, and quality standards.
- Assist with data preparation, workflow mapping, API development, system integration, user-interface development, and technical troubleshooting as assigned.
- Contribute to reusable code, templates, documentation, implementation guides, and technical patterns that support repeatable solution delivery.
- Support testing and evaluation of AI-enabled solutions, including functionality, usability, reliability, quality of outputs, and identified limitations.
- Collaborate with software engineering, cloud, cybersecurity, data, program, and customer teams to support secure and maintainable solution delivery.
- Maintain awareness of emerging AI tools, software-development practices, and technical approaches relevant to assigned work.
- Perform other duties as assigned.
Work Experience and Job Skills:
- One to four years of experience in software engineering, application development, data engineering, automation, cloud engineering, AI development, or a related technical discipline; internships, academic projects, open-source work, and relevant personal projects may be considered.
- Demonstrated proficiency in Python is required.
- Experience with C++ is required, or the demonstrated ability to become productive in C++ quickly.
- Familiarity with software-development practices, source control, testing, debugging, and technical documentation.
- Experience or exposure to APIs, JSON, databases, data structures, web services, and system integration.
- Experience or exposure to generative AI, large language models, agent frameworks, machine learning, prompt engineering, retrieval-augmented generation, or workflow automation.
- Evidence of personally building software, applications, prototypes, automations, AI agents, technical projects, or open-source contributions beyond coursework is strongly preferred.
- Ability to learn new tools and technical concepts quickly and apply them to practical use cases.
- Strong analytical and problem-solving skills, with a bias toward action and disciplined experimentation.
- Ability to work effectively in a collaborative team environment and accept technical direction, feedback, and coaching.
- Interest in customer-facing technology delivery, mission-oriented work, and solving real operational problems.
- Prior experience in a federal, public-sector, healthcare, defense, intelligence, or regulated environment is preferred but not required.
Education:
- Bachelor's Degree is required. Preferred field of study in Computer Science, Software Engineering, Information Technology, Information Systems, Data Science, Artificial Intelligence, or related field of study.
- Relevant technical coursework, projects, internships, boot camps, or certifications in software development, cloud, data, automation, or AI are preferred.
Certificates, Licenses and Registrations:
- Relevant entry-level technical certifications are preferred but not required.
- Examples may include AWS Cloud Practitioner, Microsoft Azure Fundamentals, Google Cloud Digital Leader, Python, data analytics, or AI-related certifications.
Communication Skills:
- Strong verbal and written communication skills, with the ability to explain technical work clearly to technical and nontechnical team members.
- Ability to document technical decisions, solution assumptions, defects, dependencies, and user feedback in a clear and organized manner.
- Comfortable participating in internal and customer-facing meetings, demonstrations, and technical discussions.
- Ability to write clear and concise technical content in a highly confidential manner.
Clearance Requirements:
- Eligibility to obtain and maintain a security clearance is required.
- An active DoD Top Secret clearance is desired.
Travel:
- Limited travel may be required based on customer and program needs.
Environmental Requirements:
- Mainly a routine office environment.
- May be required to lift up to ten (10) pounds.
- Flexible in working extended hours.
The above statements are intended to describe the general nature and level of work being performed by the individual(s) assigned to this position. They are not intended to be an exhaustive list of all duties, responsibilities, and skills required. Unissant management reserves the right to modify, add, or remove duties and to assign other duties as necessary. In addition, where applicable and available, reasonable accommodation(s) may be made to enable individuals with disabilities to perform essential functions of this position.
Please note: Candidate(s) will be required to go through pre-employment screening.
Unissant, Inc. is a proud Equal Opportunity Employer! (EOE; M/F/Disability/Vets)
Salary Context
This $91K-$134K range is in the lower quartile 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 Unissant, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($112K) sits 47% below the category median. Disclosed range: $91K to $134K.
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.
Unissant AI Hiring
Unissant has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Herndon, VA, US, Ashburn, VA, US. Compensation range: $134K - $218K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 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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