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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 a Senior AI Systems Architect to serve as a technical lead and enterprise architect for Artificial Intelligence (AI), Machine Learning (ML), Generative AI (Gen\-AI), and advanced analytics initiatives supporting our federal customer. This role is responsible for designing, operationalizing, and scaling secure, mission\-focused AI capabilities that enhance customer's AI/ML services, image analysis, anomaly detection, operational intelligence, and decision\-support missions.
Position Overview:
The Senior AI Systems Architect leads the design of enterprise AI ecosystems and reusable AI services within the client's AI Enablement Center, enabling AI\-as\-a\-Service capabilities across multiple applications and operational environments. The role combines enterprise architecture, AI engineering, cloud modernization, cybersecurity, and mission operations expertise to deliver scalable, secure, and governable AI solutions aligned with operational requirements and federal AI governance mandates.
This position serves as a trusted technical advisor to leadership, program stakeholders, architects, developers, data scientists, and cybersecurity teams while driving the transition of AI capabilities from research and experimentation into production operational environments.
Essential Duties and Responsibilities:
Enterprise AI Architecture \& Strategy
- Serve as the lead architect for AI and Gen\-AI initiatives supporting operational mission requirements across national security customer domains.
- Design and operationalize enterprise AI architectures supporting scalable AI\-as\-a\-Service capabilities across client applications.
- Develop reusable AI frameworks, shared services, and reference architectures aligned with customer enterprise standards and governance models.
- Architect multimodal AI solutions supporting text, image, video, and structured/unstructured data analysis.
- Define enterprise AI integration patterns supporting cloud\-native, event\-driven, and microservices\-based architectures.
AI/ML \& Gen\-AI Solution Engineering
- Lead design and deployment of AI/ML and Gen\-AI solutions including:
- + RAG architectures
+ Agentic AI frameworks
+ LLM orchestration
+ Computer Vision
+ Image analysis and object detection
+ Anomaly detection
+ Predictive analytics
+ NLP and summarization services
+ Intelligent search and semantic retrieval
- Design scalable pipelines for AI model training, inference, monitoring, retraining, and operational management.
- Implement secure Gen\-AI guardrails addressing:
- + Hallucination mitigation
+ Prompt injection prevention
+ Prompt leakage controls
+ Explainability and traceability
+ Responsible AI governance
- Architect high\-throughput AI processing frameworks capable of supporting near real\-time mission operations and large\-scale analytical workloads.
Mission Systems Integration \& Modernization
- Integrate AI services into operational systems, mission workflows, and enterprise data platforms.
- Architect event\-driven data ecosystems leveraging Kafka/MSK, APIs, streaming pipelines, and real\-time interoperability frameworks.
- Support modernization of legacy systems and data platforms into scalable cloud\-native AI\-enabled architectures.
- Design interoperable data frameworks leveraging NIEM, WCO, JSON/REST APIs, and other federal interoperability standards.
- Enable AI integration across structured and unstructured mission datasets including operational intelligence, targeting data, image repositories, and external partner feeds.
Cloud, MLOps \& DevSecOps
- Architect secure AI environments leveraging AWS and Google cloud ecosystems including:
- + Amazon Bedrock
+ AWS SageMaker
+ Vertex AI
+ Databricks
+ Containerized AI platforms
- Implement Infrastructure as Code (IaC), automated provisioning, CI/CD, and MLOps pipelines supporting rapid AI deployment and lifecycle management.
- Design AI operationalization pipelines with:
- + Automated model deployment
+ Drift monitoring
+ Retraining triggers
+ Model governance
+ Security automation
- Collaborate with DevSecOps and cybersecurity teams to integrate AI solutions into secure enterprise delivery pipelines.
Security, Governance \& Compliance
- Ensure all AI architectures comply with:
- + DHS security policies
+ NIST 800\-series standards
+ RMF requirements
+ Zero Trust principles
+ Federal AI governance guidelines
- Architect secure data access, role\-based controls, encryption, audit logging, and AI activity monitoring.
- Support ATO processes and system security documentation for AI\-enabled solutions.
- Establish governance frameworks for AI model validation, testing, explainability, and operational accountability.
Innovation \& Research
- Collaborate with customer, cloud providers, academic institutions, and emerging technology partners to evaluate and operationalize innovative AI capabilities.
- Lead technical evaluations, proof\-of\-concepts, and pilot initiatives for emerging AI technologies.
- Research and operationalize advanced AI capabilities including:
- + Agentic AI
+ Multimodal AI
+ Federated AI
+ Synthetic data generation
+ Massive parallel AI processing
+ AI\-enabled image intelligence
- Support the expansion of CBP's Technology Reference Model (TRM) for AI technologies and tools.
Technical Leadership \& Stakeholder Engagement
- Provide technical leadership and mentorship to AI engineers, data scientists, architects, and development teams.
- Lead architecture reviews, technical strategy sessions, and solution design workshops.
- Translate operational mission requirements into scalable technical architectures and implementation roadmaps.
- Develop technical white papers, executive briefings, architecture diagrams, and implementation strategies for leadership.
- Support proposal development, technical volume writing, and customer demonstrations for customer's AI initiatives.
Work Experience and Job Skills:
Required:
- 12\+ years of experience in enterprise architecture, AI/ML engineering, cloud modernization, or advanced analytics solutions.
- 5\+ years designing and implementing AI/ML or Gen\-AI solutions in enterprise or federal environments.
- Strong expertise in:
- + Generative AI
+ LLM architectures
+ RAG frameworks
+ MLOps
+ AI operationalization
+ Computer Vision
+ NLP
+ Cloud\-native architectures
- Experience with AWS and/or Google Cloud AI ecosystems.
- Strong experience designing:
- + Event\-driven architectures
+ API ecosystems
+ Data streaming platforms
+ Microservices architectures
+ Enterprise integration frameworks
- Experience implementing secure AI and DevSecOps pipelines within regulated environments.
- Strong understanding of federal cybersecurity and compliance frameworks including NIST RMF and Zero Trust.
- Experience operationalizing AI\-as\-a\-Service capabilities across mission applications.
- Experience designing scalable multimodal AI platforms supporting image intelligence, semantic search, and automated metadata generation.
- Experience reducing operational analytical processing times from hours/days to near real\-time through the implementation of massively parallel AI architectures.
- Experience implementing secure AI governance and operationalization frameworks aligned with customer AI policies and federal security mandates.
- Experience enabling reusable enterprise AI services to accelerate adoption of AI across operational domains.
Preferred:
- Experience supporting National Security or Intelligence Community programs.
- Experience with:
- + Image analysis and anomaly detection platforms
+ Operational intelligence environments
- Knowledge of:
- + NIEM
+ WCO Data Model
+ HL7/FHIR
+ Federated data architectures
+ Synthetic data generation
- Experience operationalizing multimodal AI and large\-scale AI inferencing environments.
Required Skills:
- Enterprise AI Architecture
- Generative AI \& Agentic AI
- Cloud\-Native Engineering
- MLOps \& AI Operationalization
- Mission Systems Modernization
- Event\-Driven Architecture
- Computer Vision \& Image Intelligence
- DevSecOps \& Zero Trust
- Federal Security \& Compliance
- Technical Leadership
- Innovation \& Research
- Executive Communication
Education:
- Bachelor's Degree in Computer Science, Information Technology Management or Engineering is required.
- MBA/Master's Degree in a relevant field of study preferred.
Certificates, Licenses and Registrations:
- This federal program requires the candidates to be a United States Citizen.
- Must have an active DHS clearance.
- Any related systems engineering, or related technical certifications are desired.
- SAFe Agile or cloud certifications preferred.
Communication Skills:
- Must have excellent written and verbal communication skills
- Ability to convey technical information to non\-technical individuals.
- Demonstrated experience communicating effectively across internal and external organizations.
- Must work well in a matrixed team environment.
Travel:
- This position is on\-site in Ashburn, VA
Environmental Requirements:
- Mainly sedentary; in an 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)*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Unissant AI Hiring
Unissant has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Ashburn, VA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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