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About This Role
PENDING CONTRACT AWARD WINTER 2026
Overview
Join a team where innovation meets mission. Our AI, cloud, cyber, and modernization solutions save agencies thousands of hours, safeguard national security, and strengthen health and humanitarian missions worldwide. With more than 1,700+ team members, 1,500+ AI & data experts, and 100+ prime contracts, we deliver on a scale and with purpose.
We’ve been recognized as a Top Workplace by The Washington Post for six consecutive years and named to the Inc. 5000 list of Fastest-Growing Private Companies in 13 of the past 14 years. Credence is a welcoming environment for those looking to grow and make a meaningful impact. We encourage employees to push boundaries and help solve critical, world-changing federal challenges.
Position Summary
Credence has an upcoming need for a Data Analytics and AI Integrator serves as the senior contractor authority for data analytics, automation, artificial intelligence, and machine learning initiatives across the SAF/CN support program. This position will Lead integration across cybersecurity, compliance and policy, business enterprise management, enterprise IT, and program-management activities. This position will establish a coordinated delivery approach, align initiatives with Government data and responsible-AI standards, and ensure that analytics and AI solutions are secure, explainable, reusable, measurable, and ready for Government decision or operational adoption.
Primary Responsibilities
Enterprise Leadership, Governance, and Integration
- Lead and integrate contractor Data and AI execution across SAF/CNZ, SAF/CNB, SAF/CNS, and program-management activities, coordinating priorities with the applicable division leads and the Contractor Program Manager.
- Serve as the principal contractor interface for cross-cutting Data and AI matters and coordinate with SAF/CND, SAF/CTO, system owners, Authorizing Officials, portfolio managers, infrastructure teams, and other stakeholders to align with enterprise standards and avoid duplicative or conflicting solutions.
- Establish and maintain an integrated Data and AI roadmap, use-case inventory, prioritization process, dependency map, and delivery backlog that connect initiatives to mission outcomes, performance requirements, authoritative data sources, and accountable owners.
- Lead cross-functional teams of data analysts, data engineers, data architects, cybersecurity specialists, cloud architects, automation developers, and mission subject matter experts; set priorities, resolve dependencies, review quality, and elevate delivery, security, data, or adoption risks.
- Brief senior DAF leaders on Data and AI opportunities, portfolio status, measurable benefits, resource implications, risk, adoption barriers, and recommended decisions.
Data Analytics, Engineering, and Decision Support
- Design and oversee analytics products that integrate authoritative cybersecurity, compliance, portfolio, architecture, asset, financial, operational, and infrastructure data to provide timely, decision-quality insight.
- Establish repeatable practices for data discovery, profiling, validation, cleansing, reconciliation, metadata management, lineage, quality measurement, access-control mapping, and authoritative-source designation.
- Guide development of reusable data models, taxonomies, data dictionaries, data pipelines, application programming interfaces, dashboards, scorecards, and automated workflows using Government-approved platforms.
- Apply advanced analytics to identify trends, anomalies, systemic control failures, aging actions, cost and capacity drivers, modernization candidates, and mission-risk priorities, with qualified human validation before Government use.
- Translate analytic findings into clear executive briefings, decision papers, implementation recommendations, and measurable performance indicators.
AI/ML Strategy, Delivery, and Responsible Use
- Identify and assess AI/ML opportunities across cyber compliance and auditing, eMASS and workflow automation, policy comparison, business-process optimization, portfolio and investment analysis, forecasting, architecture metadata quality, asset and license management, and enterprise IT modernization.
- Develop business cases and implementation recommendations addressing mission value, data readiness, security, privacy, records management, technical maturity, interoperability, scalability, cost, workforce impact, and acquisition or licensing implications.
- Lead approved pilots and implementations through data preparation, model or service selection, evaluation-criteria definition, testing, human-in-the-loop control design, deployment planning, user adoption, and post-deployment monitoring.
- Establish and maintain model inventories, registrations, model cards, provenance records, evaluation and bias-assessment results, human-oversight controls, and monitoring for accuracy degradation and model drift.
- Ensure all AI-assisted output is reviewed by qualified personnel and that Government data is used only with approved services, models, environments, classifications, and handling controls.
AI Security, Authorization, and Data Protection
- Integrate security and authorization considerations from inception, including AI/ML system architecture and data-flow analysis, authorization boundaries, control allocation, inherited controls, evidence requirements, and residual-risk documentation.
- Address training and inference data integrity, provenance, model versioning, adversarial inputs, prompt injection, output validation, endpoint access, secrets management, supply-chain risk, and post-deployment monitoring.
- Apply data-level protection through classification-aware tagging and handling, encryption, least privilege, attribute-based access control, privacy safeguards, records-management controls, and Zero Trust principles.
- Assess reuse of existing Government-licensed analytics, automation, cloud, and AI platforms before recommending new acquisition, and avoid unnecessary vendor lock-in or proprietary dependencies.
Requirements
- Active TS/SCI security clearance.
- Bachelor's degree or equivalent combination of education, professional training, and directly relevant experience in data science, analytics, computer science, information systems, engineering, cybersecurity, mathematics, operations research, or a related discipline.
- At least 12 years of progressively responsible experience in data analytics, data engineering or architecture, AI/ML, automation, enterprise IT, cybersecurity analytics, digital modernization, or a closely related field.
- Demonstrated experience leading multidisciplinary technical teams and coordinating enterprise initiatives across multiple organizations or mission areas in a complex Federal, DoW/DoD, DAF, or comparably regulated environment.
- Demonstrated ability to take Data or AI initiatives from use-case definition and data-readiness assessment through design, pilot, evaluation, implementation, governance, and performance monitoring.
- Working knowledge of responsible-AI, data governance, cybersecurity, privacy, records management, cloud, and Zero Trust principles sufficient to integrate specialist inputs and advise senior leaders.
- Experience developing or overseeing executive dashboards, analytical models, automated workflows, data pipelines, technical assessments, strategies, roadmaps, governance artifacts, and decision-quality briefings.
- Ability to work in a hybrid NCR environment, report to the Pentagon or other Government facilities within required response times, and perform independently without routine Government or Contract PM assistance.
Preferred Qualifications
- Graduate degree in data science, analytics, artificial intelligence, computer science, engineering, cybersecurity, information systems, operations research, or a related discipline.
- Prior experience supporting SAF/CN, Headquarters Air Force, a Service CIO/CDAO, DoW/DoD CIO, or an equivalent enterprise Data and AI governance organization.
- Project Management Professional (PMP), Certified Data Management Professional (CDMP), AWS or Azure data/cloud certification, security certification, or a recognized AI/ML credential.
- Experience with NIST AI RMF, AI system authorization, model risk management, MLOps, data catalogs, business-intelligence platforms, robotic process automation, and Government-approved generative-AI environments.
- Demonstrated experience producing measurable cycle-time, rework, cost, data-quality, risk-reduction, or decision-speed improvements through analytics, automation, or AI.
Performance Expectations
- Own the technical quality, integration, security, and timely delivery of cross-program Data and AI products and maintain a Government-visible roadmap, use-case inventory, dependency register, and benefits tracker.
- Ensure every recommended or implemented AI use case includes documented mission value, authoritative data sources, data-readiness and risk assessment, responsible-AI controls, human-review requirements, evaluation criteria, and an accountable Government decision owner.
- Maintain at least 95% accuracy and timeliness for analytics, dashboards, automation products, and decision support, correcting identified quality issues promptly and documenting material data limitations.
- Prevent the introduction of Government data into unapproved AI services or tools and ensure analytics and AI artifacts remain auditable, transferable, and supportable without dependence on undocumented contractor knowledge.
- Quantify benefits from approved analytics, automation, and AI initiatives, including hours eliminated, cycle-time reduction, rework avoided, data-quality improvement, cost savings or avoidance, and mission-risk reduction.
- Provide the Program Manager and division leads with accurate status, risks, decisions, and upcoming milestones for program reporting and senior-leader review.
Please join us, as together we build a better world one mission at a time powered by technology and its people!
Benefits
- Health Care Plan (Medical, Dental & Vision)
- Retirement Plan (401k, IRA)
- Life Insurance (Basic, Voluntary & AD&D)
- Paid Time Off (Vacation, Sick & Public Holidays)
- Family Leave (Maternity, Paternity)
- Short Term & Long Term Disability
- Training & Development
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Credence, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
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.
Credence AI Hiring
Credence has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span McLean, VA, US, Washington, DC, US, McLean, VA. Compensation range: $200K - $200K.
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/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 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).
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 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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