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About This Role
Description
The Work
ICF is seeking an Azure Operations/AI Engineer to support enterprise AI capabilities in a secure federal cloud environment. The role will provide operational and engineering support for AI services, related Azure infrastructure, and the DevSecOps practices that move AI capabilities into reliable production use.
You will work across cloud, application, security, data, and operations teams to configure and monitor AI platform components, support onboarding and access, automate delivery, troubleshoot issues, and maintain compliance evidence. The role requires someone who can connect AI and data needs to practical cloud architecture, infrastructure as code, CI/CD, monitoring, and operational support.
Job Location: Remote
What You Will Do
- Provide operational support for enterprise AI capabilities, including onboarding initiatives and users, configuring approved Azure services, and coordinating access, permissions, and dependencies.
- Translate AI and application requirements into secure, supportable cloud and infrastructure configurations across Azure environments.
- Design, implement, and maintain CI/CD workflows for AI and platform components using GitHub Enterprise, Jenkins, GitLab, or comparable technologies.
- Develop and maintain infrastructure as code using Terraform, Azure Resource Manager templates, AWS CloudFormation, or comparable tools.
- Implement logging, monitoring, alerting, and operational dashboards using Splunk, Azure Monitor, Databricks logging, AppDynamics, or comparable platforms.
- Coordinate root cause analysis across application, cloud, data, platform, and security teams; document defects, risks, corrective actions, and operational resolutions.
- Support security and compliance activities, including baseline configuration, vulnerability scanning, ATO documentation, change control, audit coordination, and remediation tracking.
- Evaluate emerging AI, cloud, and DevSecOps technologies through proof-of-concept environments and provide technically grounded recommendations.
- Support high availability, disaster recovery, backup, recovery, and continuity practices for AI and supporting infrastructure.
- Participate in Agile planning, stand-ups, reviews, and retrospectives; maintain architecture diagrams, runbooks, technical specifications, and governance documentation.
What You Will Bring With You
- 3+ years of professional experience supporting data and AI technologies, including operational support for cloud-based AI or analytics capabilities.
- Experience supporting CI/CD pipelines and Agile work management using GitHub Enterprise, Jenkins, GitLab, Azure DevOps, or comparable tools.
- Experience implementing infrastructure as code using Terraform, Azure Resource Manager templates, AWS CloudFormation, or comparable technologies.
- Hands-on experience with Azure cloud infrastructure, including resource configuration, identity and access, networking, storage, monitoring, and service integration.
- Experience with DevSecOps, cloud security, compliance governance, vulnerability remediation, and production troubleshooting in enterprise or federal environments.
- Bachelor’s or master’s degree in computer science, engineering, information technology, or equivalent combination of education and experience.
- U.S. Citizenship required due to federal contract requirements.
- Must be able to satisfy FDIC personnel eligibility and background-investigation requirements
- Candidate must reside in the U.S., be authorized to work in the U.S., and all work must be performed in the U.S.
What We Would Like You To Bring With You
- Experience supporting Azure AI services, Azure Machine Learning, Azure OpenAI, Databricks, Azure Data Factory, Azure Data Lake Storage, Azure Synapse, or related services.
- Experience with AI or machine learning environments involving Python, NLP, model serving, inference, or data preparation; familiarity with AWS SageMaker, AWS Bedrock, or comparable services.
- Experience operating enterprise DevSecOps shared services and standardizing development or deployment environments across teams.
- Experience with Kubernetes, OpenShift, Docker, Ansible, Artifactory, SonarQube, Fortify, OWASP tools, or comparable technologies.
- Experience designing high availability and disaster recovery solutions across cloud or hybrid environments.
- Experience with relational and nonrelational databases, including Oracle, SQL Server, PostgreSQL, DB2, MySQL, or comparable platforms.
- Experience with performance tuning and observability using Splunk, Azure Monitor, Databricks logs, AppDynamics, NeoLoad, or similar tools.
- Microsoft Azure Certifications
Professional Skills
- Communicate technical issues, risks, architecture decisions, and operational impacts clearly to technical and nontechnical stakeholders.
- Collaborate across cloud, AI, data, security, application, and operations teams to resolve issues and deliver reliable services.
- Use structured analysis to evaluate alternatives, identify root causes, and recommend practical improvements.
- Organize work across concurrent Agile priorities and maintain accurate technical and compliance documentation.
Bot and Third-Party Applications
Please note that this application must be submitted directly by the applicant for consideration. Failure to do so may result in the application being excluded for consideration. Applicants needing an accommodation for disability or religious purposes in connection with the application process should contact [email protected] for assistance.
Working at ICF
ICF is a global advisory and technology services provider, but we’re not your typical consultants. We combine unmatched expertise with cutting-edge technology to help clients solve their most complex challenges, navigate change, and shape the future. We can only solve the world's toughest challenges by building a workplace that allows everyone to thrive. We are an equal opportunity employer. Together, our employees are empowered to share their expertise and collaborate with others to achieve personal and professional goals. For more information, please read our EEO policy.
We will consider for employment qualified applicants with arrest and conviction records.
Reasonable Accommodations are available, including, but not limited to, for disabled veterans, individuals with disabilities, and individuals with sincerely held religious beliefs, in all phases of the application and employment process. To request an accommodation, please email [email protected] and we will be happy to assist. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
Read more about workplace discrimination rights or our benefit offerings which are included in the Transparency in (Benefits) Coverage Act.
Candidate AI Usage Policy At ICF, we are committed to ensuring a fair interview process for all candidates based on their own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) tools to generate or assist with responses during interviews (whether in-person or virtual) is not permitted. This policy is in place to maintain the integrity and authenticity of the interview process.
However, we understand that some candidates may require accommodation that involves the use of AI. If such an accommodation is needed, candidates are instructed to contact us in advance at [email protected]. We are dedicated to providing the necessary support to ensure that all candidates have an equal opportunity to succeed.
Pay Range - There are multiple factors that are considered in determining final pay for a position, including, but not limited to, relevant work experience, skills, certifications and competencies that align to the specified role, geographic location, education and certifications as well as contract provisions regarding labor categories that are specific to the position.
The pay range for this position based on full-time employment is:
$108,476.00 - $184,409.00 Nationwide Remote Office (US99)
Salary Context
This $108K-$184K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At ICF, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($146K) sits 31% below the category median. Disclosed range: $108K to $184K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
ICF AI Hiring
ICF has 7 open AI roles right now. They're hiring across MLOps Engineer, AI Product Manager, Data Scientist, AI/ML Engineer. Positions span Reston, VA, US, Montgomery, AL, Rockville, MD, US. Compensation range: $125K - $184K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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