AI Engineer

ICF is seeking an AI Engineer to help develop and operationalize AI and machine learning capabilities for a cloud-based federal enterprise data and analytics platform. In this role, you will build reusable AI/ML and NLP solutions, support MLOps pipelines for deployment, and help deliver governed, production-ready generative AI at scale.

Role Focus

• Develop NLP and text analytics capabilities, including prompt orchestration, guardrails, content-filtering approaches, and human-in-the-loop workflows using VA-approved AI/ML tools and data environments.
• Operationalize unstructured-data use cases into repeatable technical patterns covering privacy, provenance, evaluation, monitoring, and operational integration.
• Enable trustworthy generative AI by partnering with Trustworthy AI, Security, Data Governance, and Customer Experience personnel to ensure capabilities are safe, useful, and governed.
• Apply LLM and GenAI patterns such as retrieval and evaluation approaches, guardrails, content filters, and human-in-the-loop workflows, leveraging Azure AI and Azure OpenAI to support delivery.

Collaboration and Documentation

• Collaborate with 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.

Required 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 perform all work in the U.S.
• 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 designing, developing, and deploying AI/ML solutions in enterprise cloud environments.

Preferred Qualifications

• Hands-on experience with LLMs, generative AI, 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 and evaluation frameworks, including model testing and AI application monitoring solutions.
• Experience implementing AI safety controls such as content filtering, guardrails, human-in-the-loop review processes, and Trustworthy AI practices.
• Experience creating and maintaining MLOps pipelines, model deployment and versioning, performance monitoring, and automated retraining processes.
• Experience integrating AI solutions with cloud-native data pipelines, analytics platforms, APIs, and operational business applications.
• Experience supporting predictive analytics and generative AI use cases including document intelligence, text analytics, classification, summarization, recommendation.
• 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 using GitHub.
• Experience working in Agile, cross-functional teams across engineering, architecture, data science, product, governance, and business users.
• Experience supporting Federal government, healthcare, or other highly regulated environments is preferred.

Technologies

• Databricks ML, Azure Machine Learning, MLflow, Python, NLP
• Generative AI, LLM, GenAI, prompt orchestration, agentic workflows
• MLOps pipelines, VA-approved AI/ML tools, Azure AI, Azure OpenAI
• LangChain, Semantic Kernel, retrieval-augmented generation (RAG), semantic search
• Vector databases, Databricks AI/ML, Delta Lake
• Cloud-scale data platforms, text analytics, retrieval/evaluation patterns
• Guardrails, content filtering, human-in-the-loop workflows, model monitoring
• Performance evaluation, cost optimization, GitHub, CI/CD, SQL

Location

• Remote (strong preference for candidates living in the Washington DC Metro Area)
• Occasional onsite meetings on the client site in Washington, DC

Compensation

• $108,476 - $184,409 per year (based on full-time employment)
• Nationwide Remote Office (US99)

Additional Information

• Reasonable accommodations are available. To request an accommodation, email [email protected].
• AI tools may not be used to generate or assist with responses during interviews (in-person or virtual). Accommodation requests involving AI should be coordinated in advance with [email protected].
• This application must be submitted directly by the applicant for consideration.