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About This Role
Description
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 1,700+ team members, 1,500+ AI/data experts, and 100+ prime contracts, we deliver at scale and with purpose.
We’ve been recognized as a Top Workplace by the Washington Post for six straight years and named to the Inc. 5000 Fastest Growing Private Companies 13 of the past 14 years. Credence is a welcoming home for those looking to grow and contribute to positive change. We encourage all employees to expand beyond their boundaries, dive into important world-changing Federal challenges.
Credence has an immediate need for an AI Platform & DevSecOps Engineer to support federal programs hosted within AWS GovCloud. This hands-on engineering role combines DevSecOps, cloud infrastructure, platform engineering, security automation, and AI platform enablement to support secure and reliable delivery of mission-critical applications.
The engineer will design, automate, secure, and operate cloud infrastructure, CI/CD pipelines, container platforms, and shared platform services while supporting the infrastructure and operational capabilities needed for AI-enabled applications and services.
The ideal candidate has a strong foundation in AWS, Kubernetes, Infrastructure as Code (IaC), CI/CD, Python, security automation, and production operations, along with practical knowledge of modern AI technologies and the infrastructure patterns used to securely deploy and operate AI-enabled solutions.
Core Responsibilities
DevSecOps & Platform Engineering: Design, build, and maintain secure, scalable platform capabilities supporting application development and delivery within AWS GovCloud. Develop reusable infrastructure, automation, deployment patterns, and platform services that improve engineering consistency, security, and developer productivity.
AWS GovCloud Architecture & Management: Design, implement, and maintain secure, scalable, and compliant AWS GovCloud environments supporting DoD and federal applications. Work with AWS services including Amazon EKS, ECS/Fargate, IAM, VPC, S3, CloudWatch, and other approved cloud-native services.
DevSecOps Pipeline Development: Build, maintain, and improve CI/CD pipelines using technologies such as GitLab CI/CD, Jenkins, AWS CodePipeline, and related tooling. Automate application and infrastructure deployments while integrating testing, security scanning, compliance checks, and deployment controls into delivery workflows.
Infrastructure as Code (IaC): Automate infrastructure provisioning and configuration using technologies such as Terraform, CloudFormation, and Ansible. Develop reusable infrastructure components and apply version control, peer review, automated validation, and repeatable deployment practices.
Containerization & Orchestration: Deploy and operate containerized applications using Docker/OCI containers and Kubernetes, including Amazon EKS. Support workload configuration, networking, storage, secrets, scaling, resource management, upgrades, security, and troubleshooting.
Security & Compliance: Integrate security throughout the software development lifecycle and support compliance with applicable federal cybersecurity frameworks, including NIST 800-53, NIST 800-171, RMF, FedRAMP, STIGs, and Zero Trust principles. Implement and support automated security capabilities including SAST/DAST, software composition analysis, container scanning, IaC scanning, secrets detection, vulnerability management, and compliance validation.
AI Platform Enablement: Support the cloud infrastructure and shared platform capabilities required to securely deploy and operate AI-enabled applications and services. Work with AI and software engineering teams to establish repeatable patterns for deployment, configuration, access control, security, monitoring, scalability, and operational support.
AI/LLM Service Integration: Support the secure integration and operation of approved AI and LLM services, including platforms such as Amazon Bedrock and other enterprise AI services. Assist engineering teams with infrastructure, authentication and authorization, API connectivity, configuration, deployment automation, monitoring, and operational troubleshooting.
Monitoring, Observability & Incident Response: Implement monitoring, logging, tracing, dashboards, and alerting across cloud infrastructure, Kubernetes, applications, and platform services using technologies such as AWS CloudWatch, Security Hub, GuardDuty, Splunk/ELK, Prometheus, Grafana, OpenTelemetry, or comparable solutions. Participate in incident response, root-cause analysis, vulnerability remediation, and continuous reliability improvements.
Automation & Scripting: Develop automation and platform tooling using Python, Bash, PowerShell, or similar technologies to eliminate repetitive operational tasks, integrate services and APIs, enforce platform standards, and improve deployment efficiency.
Production Operations: Troubleshoot complex infrastructure, application, CI/CD, Kubernetes, networking, and cloud service issues in production and production-like environments. Support operational readiness, system reliability, capacity, performance, and continuous improvement.
Collaboration & Knowledge Sharing: Work closely with software engineers, AI engineers, cybersecurity teams, cloud engineers, architects, and mission stakeholders to integrate security, automation, and platform capabilities throughout the software development lifecycle. Develop technical documentation, architecture diagrams, runbooks, and reusable engineering standards.
Why This Role Matters
Mission Impact — Build and operate secure cloud and DevSecOps capabilities that directly support mission-critical federal applications and emerging AI-enabled solutions.
Secure AI Enablement — Help bridge the gap between AI development and production operations by providing secure, automated, observable, and repeatable platform capabilities for deploying AI-enabled applications within federal environments.
Modern Platform Engineering — Work across AWS GovCloud, Kubernetes, Infrastructure as Code, CI/CD, automation, cybersecurity, observability, and emerging AI technologies while helping modernize how applications are delivered and operated.
Cross-Functional Engineering — Collaborate with DevSecOps, software, AI, cloud, and cybersecurity engineers to solve complex technical challenges without being limited to a single technology domain.
Continuous Growth — Expand existing DevSecOps and cloud expertise into AI platform engineering while continuing to deepen skills in automation, security, cloud-native technologies, and production operations.
Requirements
What You Bring* Bachelor’s degree in Computer Science, Information Technology, Engineering, Cybersecurity, or a related technical field, or equivalent relevant professional experience.
- 5+ years of hands-on experience in DevSecOps, DevOps, cloud engineering, platform engineering, software engineering, or related technical roles.
- Strong hands-on experience designing, deploying, or operating workloads within AWS; AWS GovCloud experience is highly desirable.
- Hands-on experience with Docker/OCI containers and Kubernetes, preferably Amazon EKS.
- Experience developing and maintaining CI/CD pipelines using GitLab CI/CD, Jenkins, AWS CodePipeline, or comparable technologies.
- Strong experience with Infrastructure as Code, including Terraform, CloudFormation, or comparable technologies.
- Proficiency with Python and experience using Bash, PowerShell, or similar scripting languages for automation.
- Experience with Git-based development workflows, automated testing, artifact management, and modern software delivery practices.
- Experience integrating security capabilities into CI/CD and cloud environments, including vulnerability scanning, secrets management, IAM, secure configuration, and automated security controls.
- Experience implementing monitoring, logging, alerting, and operational troubleshooting for production or production-like environments.
- Working knowledge of AI/ML and generative AI technologies, including LLMs, AI APIs, model inference, and cloud infrastructure supporting AI-enabled applications.
- Experience supporting, deploying, integrating, or operating AI/ML or AI-enabled applications in cloud or containerized environments.
- Understanding of cloud security principles, identity and access management, networking, encryption, secrets management, and least-privilege access.
- Strong troubleshooting, technical communication, documentation, and cross-functional collaboration skills.
- U.S. Citizenship with eligibility for a DoD Secret clearance.
Preferred
- Experience supporting DoD, federal civilian, or other highly regulated environments.
- Hands-on experience with AWS GovCloud.
- Knowledge of federal cybersecurity requirements and frameworks including RMF, NIST 800-53, NIST 800-171, FedRAMP, DISA STIGs, and Zero Trust.
- Experience with Amazon Bedrock, Amazon Sagemaker, or other enterprise AI/ML platforms and services.
- Familiarity with operational considerations for AI workloads, including security, observability, scalability, availability, performance, and cost.
- Experience implementing CI/CD or platform automation supporting AI-enabled applications and services.
- Familiarity with AI security and governance considerations within federal or regulated environments.
- Experience with Helm, Argo CD, Flux, or other Kubernetes/GitOps technologies.
- Experience with observability technologies such as CloudWatch, Prometheus, Grafana, OpenTelemetry, Splunk, or ELK.
- Experience with software supply-chain security practices such as SBOM generation, artifact signing, provenance, container hardening, and policy enforcement.
- Experience with secrets-management and security technologies such as AWS Secrets Manager, AWS KMS, HashiCorp Vault, or comparable solutions.
- AWS, Kubernetes, security, DevSecOps, or AI/ML-related professional certifications.
Salary Range
$150,000 - $190,000 annually. Actual compensation will be determined based on the selected candidate’s experience, education, certifications, skills, location, and overall qualifications.
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
- Wellness Resources
Salary Context
This $140K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 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. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $140K to $200K.
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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