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About This Role
Unissant 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 Principal AI Architect and Enterprise AI Transformation Lead to join our team and support our federal client in Ashburn, VA.
The ideal candidate is a hands-on AI leader who combines deep technical expertise with enterprise architecture, AI strategy, and customer engagement. This individual will lead the adoption and operationalization of AI capabilities while personally shaping solution architecture, developing prototypes, and guiding secure deployment in customer environments.
The successful candidate will bring hands-on experience from a commercial AI company or comparable fast-moving commercial AI environment and apply those practices to secure federal missions. This individual will build and demonstrate AI solutions using Generative AI, Large Language Models, Agentic AI, Retrieval-Augmented Generation, machine learning, and cloud-native technologies, and must be equally comfortable writing code, designing enterprise architectures, leading customer demonstrations and workshops, guiding technical teams, and advising executives on AI strategy, adoption, and measurable mission value.
Essential Duties and Responsibilities
- Lead enterprise AI strategy, architecture, adoption, and roadmaps while translating priorities into executable initiatives across programs, customers, and technical teams.
- Identify high-value opportunities where AI can improve mission performance, decision-making, operational efficiency, automation, and customer services.
- Evaluate foundation models, AI platforms, development frameworks, and emerging technologies through hands-on experimentation and benchmarking, then recommend practical options based on mission fit, security, scalability, cost, and implementation risk.
- Rapidly design, build, test, and demonstrate AI and machine learning prototypes that validate high-value mission and business use cases and establish a path to production.
- Convert validated prototypes into secure, scalable, production-ready solutions using Generative AI, Large Language Models, Agentic AI, Retrieval-Augmented Generation, intelligent automation, and related enterprise AI technologies.
- Develop AI applications and services using Python, APIs, cloud-native services, and modern AI frameworks.
- Design and implement enterprise RAG solutions, including document ingestion, embeddings, vector databases, semantic search, grounding, retrieval, orchestration, and response evaluation.
- Design and develop AI agents and multi-agent workflows that integrate with enterprise applications, data sources, APIs, and business processes.
- Architect secure and scalable AI platforms across hybrid-cloud, multi-cloud, and on-premises environments.
- Design and implement MLOps and GenAIOps pipelines supporting model development, deployment, evaluation, monitoring, governance, and lifecycle management.
- Build and integrate data pipelines, vector databases, model endpoints, agent frameworks, APIs, and cloud-native AI services.
- Establish technical approaches for prompt engineering, model selection, fine-tuning, grounding, human-in-the-loop workflows, guardrails, and AI evaluation.
- Partner with engineering, cybersecurity, data, cloud, DevSecOps, operations, and enterprise architecture teams to integrate AI capabilities with existing systems and target-state architectures.
- Conduct architecture reviews, code reviews, technical assessments, and solution evaluations to ensure alignment with security, privacy, governance, interoperability, performance, and scalability requirements.
- Define reusable AI reference architectures, development patterns, technical standards, and implementation best practices.
- Provide hands-on technical leadership by contributing to solution design and implementation, resolving complex technical issues, reviewing engineering work, and mentoring delivery teams.
- Lead customer demonstrations, technical workshops, discovery sessions, solution reviews, and executive briefings that translate AI capabilities into clear mission use cases, adoption paths, and measurable business value.
- Provide technical leadership for business development, capture, proposals, solutioning, and customer demonstrations by shaping credible AI approaches, architectures, prototypes, and implementation plans.
- Apply commercial AI product-development, rapid experimentation, and deployment practices from a commercial AI company or comparable fast-moving technology organization to accelerate secure AI adoption in federal and national security environments.
- Remain current on developments in Generative AI, Agentic AI, machine learning, cloud AI platforms, data engineering, and responsible AI.
Required Experience and Qualifications
- Minimum 15 years of progressive experience in software engineering, enterprise architecture, cloud architecture, systems engineering, data engineering, or related technical disciplines, including leadership of complex enterprise technology initiatives.
- Minimum 8 years of experience designing and delivering AI, machine learning, data science, or advanced analytics solutions, including recent hands-on experience building and deploying enterprise AI applications.
- Demonstrated success serving as a Principal AI Architect, Enterprise AI Architect, AI Solutions Architect, Chief Architect, Lead AI Engineer, or equivalent senior technical leader responsible for enterprise-scale AI solutions.
- Demonstrated experience personally designing, coding, prototyping, and deploying functional AI applications while also providing architecture and strategic leadership.
- Demonstrated hands-on experience at a commercial AI company or comparable commercial technology organization developing, implementing, or scaling AI products and solutions; experience applying commercial AI practices within federal, national security, regulated, or mission-critical environments is strongly preferred.
- Advanced hands-on programming experience with Python and relevant AI, machine learning, data science, and API development libraries.
- Experience developing Generative AI applications using Large Language Models, prompt engineering, Retrieval-Augmented Generation, tool calling, AI agents, and orchestration frameworks.
- Hands-on experience with leading enterprise AI and machine learning platforms and frameworks, such as Azure AI, AWS Bedrock, Amazon SageMaker, Databricks, MLflow, Hugging Face, LangChain, LangGraph, LlamaIndex, or comparable technologies.
- Experience designing and implementing vector databases, embedding pipelines, semantic search, knowledge retrieval, document processing, and enterprise RAG solutions.
- Experience designing autonomous or human-supervised AI agents and multi-agent workflows.
- Experience implementing AI evaluation methods for accuracy, groundedness, relevance, safety, latency, reliability, and overall solution performance.
- Experience implementing MLOps, GenAIOps, model monitoring, AI governance, and automated deployment pipelines.
- Experience with Docker, Kubernetes, APIs, microservices, CI/CD pipelines, infrastructure automation, and cloud-native application development.
- Strong understanding of machine learning, deep learning, natural language processing, Generative AI, Agentic AI, data science, and enterprise architecture.
- Experience architecting complex hybrid-cloud, multi-cloud, and on-premises environments supporting regulated or mission-critical operations.
- Experience developing enterprise roadmaps, reference architectures, technology standards, reusable solution patterns, and technical governance processes.
- Strong knowledge of enterprise architecture frameworks such as TOGAF, FEAF, DoDAF, Zachman, or equivalent methodologies.
- Strong understanding of responsible AI, cybersecurity, data privacy, access controls, model risk, compliance, and secure AI development practices.
- Demonstrated ability to move AI solutions from experimentation and proof of concept through deployment and operational adoption.
- Strong executive presence and the ability to translate complex AI concepts into clear recommendations, tradeoffs, risks, adoption plans, and business or mission value for senior stakeholders.
- Excellent written and verbal communication skills with the ability to engage technical and executive audiences.
- Proven ability to influence without direct authority and work effectively across commercial AI organizations, federal customers, internal teams, partners, and external organizations.
Education
- Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Mathematics, Information Systems, or a related field is required.
- A master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline is preferred.
Communication Skills
- Excellent verbal and written communication skills with the ability to present complex technical concepts clearly and effectively.
- Ability to interface with, inspire, and influence stakeholders at multiple levels of the organization.
- Ability to communicate complex technical information clearly to technical, non-technical, and executive audiences.
- Strong ability to lead technical workshops, architecture discussions, customer demonstrations, and executive briefings.
- Ability to translate business and mission requirements into practical AI architectures and implementation plans.
Clearance Requirements
- DHS CBP or TS/SCI clearance is required.
- Active security clearance is preferred.
Work Location and Schedule
- Local candidates are required.
- Must be onsite in Ashburn, VA five days per week for the first year.
Environmental Requirements
- Mainly a routine office environment.
- May be required to lift up to ten (10) pounds.
- Ability to work extended hours when required.
- Eastern Time work hours aligned with the client workday.
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 $153K-$218K range is above the median for AI Architect roles in our dataset (median: $179K across 32 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,653 AI roles we're tracking, AI Architect positions make up 1% of the market. At Unissant, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $230,000 based on 212 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($185K) sits 19% below the category median. Disclosed range: $153K to $218K.
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.
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% (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 Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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