AI/ML Engineer - Data Full-Stack Solutions
Role overview
JOB TITLE: AI/ML Engineer – Data & Full-Stack Solutions
POSITION INFORMATION: Full-Time Position
LOCATION: Fairfax, VA; Hybrid – 3 days onsite/2 remote
POSITION TIMING: ASAP; hiring immediately
BENEFITS: Health, Dental, and Vision, 401(k), Tuition Reimbursement, Flexible Spending Account (FSA), 11 Paid Federal Holidays, 3 weeks Paid Time Off
ITC Federal, LLC (ITC) connects technology advancements in automation and AI, customer experience, and financial services to solve government mission challenges, enabling smoother operational efficiency and bolstering national security. We leverage the latest technology innovations and proven approaches to better serve the mission and support the DHS, DOJ, and DoW workforce, customers, and programs, regardless of scale or complexity. ITC is located in Fairfax, VA and offers outstanding compensation and benefits plan and a challenging and rewarding professional work environment.
POSITION OVERVIEW:
ITC Federal is seeking a highly skilled AI/ML Engineer with a strong background in data engineering and full-stack development to support the design, development, and deployment of advanced AI-driven solutions. This role requires a unique blend of technical expertise and the ability to translate complex concepts into clear, customer-facing deliverables, including documentation, proposals, and solution artifacts.
The ideal candidate will have experience taking ideas from concept to production—building intelligent, scalable solutions and integrating them into enterprise environments to drive meaningful business outcomes. This position works closely with cross-functional teams to deliver innovative, production-ready capabilities leveraging modern cloud and AI technologies.
Responsibilities
- check_circle Design, develop, and deploy AI/ML solutions, including LLMs, NLP, computer vision, and predictive analytics
- check_circle Build and implement Retrieval-Augmented Generation (RAG) pipelines and AI-powered applications
- check_circle Integrate AI solutions into existing enterprise systems and workflows
- check_circle Design and implement scalable data pipelines for ingestion, transformation, and processing of structured and unstructured data
- check_circle Develop and maintain data lakehouse architectures
- check_circle Build ETL/ELT workflows using tools such as Azure Data Factory, Apache Airflow, or similar
- check_circle Develop end-to-end applications, including frontend, backend, and APIs
- check_circle Create intuitive user interfaces for AI-driven applications
- check_circle Deploy applications using cloud-native architectures (AWS, Azure, GCP)
- check_circle Translate business requirements into technical architectures and working solutions
- check_circle Integrate AI and data solutions into enterprise and cloud environments
- check_circle Collaborate with cross-functional teams to deliver production-ready solutions
- check_circle Clearly articulate complex technical concepts through written documentation, proposals, and solution artifacts
- check_circle Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field
- check_circle 4+ years of experience in AI/ML, data engineering, or full-stack development
- check_circle Strong programming skills in Python, JavaScript/TypeScript, or Java
- check_circle Experience with AI/ML frameworks (TensorFlow, PyTorch, Hugging Face, OpenAI APIs)
- check_circle Experience building data pipelines and working with big data technologies
- check_circle Hands-on experience with cloud platforms (AWS, Azure, or GCP)
- check_circle Experience with REST APIs, microservices, and frontend frameworks (React, Angular, or Vue)
- check_circle Experience with LLMs, RAG architectures, and generative AI applications
- check_circle Familiarity with Databricks, Snowflake, or lakehouse architectures
- check_circle Experience with DevOps tools (Docker, Kubernetes, CI/CD pipelines)
Preferred qualifications
- Experience supporting federal government or GovCon environments
- Experience contributing to proposals, technical responses, or solutioning efforts (e.g., white papers, RFP responses, technical volumes)