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About This Role
StraitSys Inc
Regular
PRIMARY FUNCTION
StraitSys is seeking a Technical Innovation Subject Matter Expert in Artificial Intelligence to support the FBI in Quantico, Virginia. This position will provide strategic and technical leadership in the design, development, implementation, and support of enterprise technology solutions leveraging Artificial Intelligence, Machine Learning (ML), and Generative AI technologies. This position leads technical teams in delivering innovative, secure, scalable, and responsible AI solutions that enhance business operations, improve customer experiences, automate workflows, and support data\-driven decision making.
ESSENTIAL FUNCTIONS
Facilitate the definition of project charter, scope, goals, and deliverables.
Lead architecture, design, implementation, and support of Generative AI solutions.
Provide technical leadership for AI initiatives involving Machine Learning, Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP), intelligent automation, and predictive analytics.
Design and implement GenAI solutions using commercial and open\-source foundation models to improve business processes, employee productivity, and customer engagement.
Develop AI\-enabled applications using Retrieval\-Augmented Generation (RAG), vector databases, prompt engineering, AI agents, semantic search, and orchestration frameworks.
Evaluate emerging AI technologies, models, and platforms, recommending solutions that align with organizational goals and enterprise architecture.
Lead proof\-of\-concepts (POCs), pilot programs, and production deployments of AI and GenAI capabilities.
Collaborate with business leaders, developers, data engineers, cybersecurity teams, data scientists, and vendors to identify and implement AI use cases.
Establish enterprise standards for AI architecture, model governance, responsible AI, prompt management, and AI lifecycle management.
Ensure AI and GenAI solutions comply with cybersecurity requirements, privacy regulations, ethical AI principles, and organizational governance policies.
Lead integration of AI services with enterprise applications, APIs, cloud platforms, collaboration platforms, and business workflows.
Mentor technical staff on AI development practices, prompt engineering techniques, AI governance, and emerging technologies.
Develop technology roadmaps supporting enterprise AI adoption and digital transformation initiatives.
Stay current with advances in Generative AI, foundation models, cloud AI services, and industry best practices.
Assist the Government in creating and compiling project plans and work assignments, project schedules and milestones for new innovation, and budgetary documentation; facilitating and monitoring work efforts; identifying resource trends and needs; and escalating functional, quality, or timeline issues appropriately.
Work closely with internal and government stakeholders to understand negotiated, documented, and agreed upon request deliverables.
Assist the Government in assessing, validating, monitoring, planning, and controlling/mitigating project change and risk.
Communicate progress, status, and issues to customers and management.
Assist the Government in measuring project performance using appropriate systems, tools, and techniques.
Assist the Government in managing customer expectations and maintaining ongoing client satisfaction.
Plan, facilitate and champion effective Agile / Scrum ceremonies including backlog grooming, sprint planning, stand ups, sprint demos and retrospectives.
Analyze new and complex project related problems and create innovative solutions involving finance, scheduling, technology, methodology, tools, and solution components.
SUPERVISORY RESPONSIBILITIES:
Yes
KNOWLEDGE, SKILLS, \& ABILITIES:
Demonstrated project management disciplines and governance, Agile/Scrum\-based practices, and DevSecOps, with demonstrated progression of increased scope and complexity.
Knowledge to develop and manage highly scalable cloud\-based systems.
Ability to present effective executive level and decision\-making presentations and ability to explain complex technical concepts in pictures and words that are understandable to a broad audience.
Commitment to exceptional customer collaboration and engagement techniques.
Proven success in managing multiple priorities with follow\-through on projects/tasks to completion; must manage multiple tasks independently and prioritize to meet business needs.
Prior success in an Information Technology Service Management environment.
Must be able to establish and maintain collaborative working relationships.
Must communicate clearly and concisely, orally and in writing, with users and technical support.
Must be willing to serve as team lead for the contractor staff assigned to the Task Order
QUALIFICATIONS:
Must be a US Citizen.
Active Top Secret Clearance; may be required to obtain SCI access
Bachelor's degree from an accredited college or university with major coursework in business, computer science, project management, information technology, or a related field. Significant related experience may be considered in lieu of a bachelor's degree.
Advanced degree in a business or technical field preferred
Hands\-on experience with one or more enterprise AI platforms, including Microsoft Azure AI, Azure OpenAI Service, Amazon Bedrock, AWS AI Services, Google Vertex AI, or equivalent technologies.
Experience integrating Large Language Models (LLMs) into enterprise applications using APIs and cloud services.
Experience with Claude, OpenAI and Microsoft Copilot is a plus.
Demonstrated experience with:
Generative AI applications
Prompt engineering
Retrieval\-Augmented Generation (RAG)
AI agents and workflow automation
Natural Language Processing (NLP)
Machine Learning
Semantic search and vector databases
AI model evaluation and optimization
Experience developing secure, scalable cloud\-based AI solutions.
Strong understanding of cybersecurity, responsible AI, data governance, privacy, and compliance requirements.
Excellent leadership, communication, analytical, and problem\-solving skills.
Any of the certifications below are highly desirable:
FAC P/PM or PMP certification preferred.
Microsoft Certified: Azure AI Engineer Associate
Microsoft Certified: Azure Solutions Architect Expert
Microsoft Applied Skills: Build Generative AI Solutions with Azure OpenAI Service
Microsoft Applied Skills: Create AI Agents with Azure AI Foundry
AWS Certified Machine Learning – Specialty
Google Professional Machine Learning Engineer
CompTIA AI Essentials\+ (or equivalent AI certification)
Industry\-recognized IT service management certification preferred.
Experience with Atlassian / Jira / Confluence and TFS a plus
Project Management experience preferred, not required.
Knowledge of cybersecurity principles preferred, not required.
Ability to successfully pass a pre\-employment drug test.
PREFERENCE STATEMENT
Preference will be given to Calista shareholders and their descendants and to spouses of Calista shareholders, and to shareholders of other corporations created pursuant to the Alaska Native Claims Settlement Act, in accordance with Title 43 U.S. Code 1626(g).
EEO STATEMENT
Additionally, it is our policy to select, place, train and promote the most qualified individuals based upon relevant factors such as work quality, attitude and experience, so as to provide equal employment opportunity for all employees in compliance with applicable local, state and federal laws and without regard to non\-work related factors such as race, color, religion/creed, sex, national origin, age, disability, marital status, veteran status, pregnancy, sexual orientation, gender identity, citizenship, genetic information, or other protected status. When applicable, our policy of non\-discrimination applies to all terms and conditions of employment, including but not limited to, recruiting, hiring, training, transfer, promotion, placement, layoff, compensation, termination, reduction in force and benefits.
REASONABLE ACCOMMODATION
It is Calista and Subsidiaries' business philosophy and practice to provide reasonable accommodations, according to applicable state and federal laws, to all qualified individuals with physical or mental disabilities.
The statements contained in this job description are intended to describe the general content and requirements for performance of this job. It is not intended to be an exhaustive list of all job duties, responsibilities, and requirements.
This job description is not an employment agreement or contract. Management has the exclusive right to alter the scope of work within the framework of this job description at any time without prior notice.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Yulista Management Services, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Yulista Management Services AI Hiring
Yulista Management Services has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Quantico, VA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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