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About This Role
Description
Please note: This role is contingent upon a contract award. While it is not an immediate opening, we are actively conducting interviews and extending offers in anticipation of the award.
The Work
At ICF our Digital Modernization Division is an information technology and management consulting organization that delivers integrated, strategic solutions to federal clients. We bring expertise in cloud, cybersecurity, enterprise architecture, data modernization, and digital transformation to support mission-critical government programs.
Join a team accelerating the modernization of a large federal agency's enterprise data and analytics ecosystem. This cloud-based platform provides data storage, analytics, governance, and AI/ML capabilities that enable thousands of users to transform data into actionable insights. As demand continues to grow, the team is focused on migrating legacy workloads, streamlining onboarding and support, expanding platform capabilities, and helping organizations across the enterprise adopt modern data and AI solutions at scale.
The AI Engineer develops and operationalizes AI, machine learning, NLP, predictive analytics, and generative AI capabilities that accelerate data-driven decision making across the enterprise. This role builds reusable AI/ML solutions, model deployment frameworks, prompt orchestration patterns, agentic workflows, and MLOps pipelines leveraging Databricks ML, Azure Machine Learning, MLflow, Python, and cloud-scale data platforms. Working across engineering, governance, security, and business teams, the AI Engineer enables the adoption of AI at scale through model monitoring, performance evaluation, human-in-the-loop workflows, cost optimization, and responsible AI practices that ensure solutions are trusted, governed, and production-ready.
Job Location: Remote, however, strong preference for candidates who live in the Washington DC Metro Area. There will be occasional onsite meetings on the client site in Washington, DC.
- If you accept this position, you should note that ICF does monitor employee work locations, blocks access from foreign locations/foreign IP addresses, and prohibits personal VPN connections.
What You Will Do:
- Develops NLP, text analytics, prompt orchestration, guardrails, content-filtering approaches, and human-in-the-loop workflows using VA-approved AI/ML tools and data environments.
- Translates unstructured-data use cases into repeatable technical patterns that address privacy, provenance, evaluation, monitoring, and operational integration.
- Works with Trustworthy AI, Security, Data Governance, and Customer Experience personnel to ensure generative-AI capabilities are safe, useful, and governed.
- Apply NLP, LLM/GenAI patterns, prompt orchestration, retrieval/evaluation patterns, guardrails, content filters, human-in-the-loop workflows, and Azure AI to support role delivery.
- Collaborate with relevant product, engineering, security, governance, quality, and customer-facing stakeholders as required by the role.
- Document work products, decisions, risks, and delivery evidence to support traceability and continuous improvement.
Basic Qualifications
- U.S. Citizenship is required due to federal contract 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.
- Candidate must have lived in the U.S. for three (3) full years out of the last five (5) years.
- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related field; Master's degree may substitute for two (2) years of relevant experience.
- 6+ years of designing, developing, and deploying AI/ML solutions in enterprise cloud environments.
Preferred Qualifications
- Hands-on experience with large language models (LLMs), generative AI, natural language processing (NLP), retrieval-augmented generation (RAG), semantic search, vector databases, and prompt engineering techniques.
- Experience building AI applications and agentic workflows using Azure AI Services, Azure OpenAI, Databricks AI/ML, MLflow, LangChain, Semantic Kernel, or similar frameworks.
- Experience developing prompt orchestration, evaluation frameworks, model testing strategies, and AI application monitoring solutions.
- Experience implementing AI safety controls, content filtering, guardrails, human-in-the-loop review processes, and Trustworthy AI practices.
- Experience developing and maintaining MLOps pipelines, model deployment frameworks, model versioning, performance monitoring, and automated retraining processes.
- Strong programming experience using Python, SQL, and modern AI/ML libraries and frameworks.
- Experience with Databricks, Delta Lake, Azure Machine Learning, and enterprise-scale data platforms.
- Experience integrating AI solutions with cloud-native data pipelines, analytics platforms, APIs, and operational business applications.
- Experience supporting predictive analytics, document intelligence, text analytics, classification, summarization, recommendation, and generative AI use cases.
- Familiarity with enterprise data governance, metadata management, data lineage, privacy controls, and role-based access models in regulated environments.
- Experience optimizing AI/ML workloads for performance, scalability, reliability, and cloud cost management.
- Experience with CI/CD practices utilizing GitHub
- Experience working in Agile, cross-functional teams consisting of engineers, architects, data scientists, product owners, governance stakeholders, and business users.
- Experience supporting Federal government, healthcare, or other highly regulated environments is preferred.
Professional Skills:
- Highly effective analytical, problem-solving, and decision-making capabilities.
- Excellent written and verbal communication skills, with the ability to work effectively across technical and non-technical audiences.
- Strong organization, attention to detail, and the ability to prioritize and manage multiple responsibilities.
- Collaborative approach with a commitment to quality, accountability, and continuous improvement.
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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 2239 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 11,520 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 $212,040 based on 13,780 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 $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; 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, Rockville, MD, US. Compensation range: $125K - $184K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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