AI​/ML Engineer Security Clearance

AI/ML Engineer

Location:

DC/MD/VA (Preferred | Hybrid/Remote considered)

Prime:
Accenture Federal Services (AFS)

Clearance: U.S. Citizenship required; active Secret/TS/SCI preferred; ability to obtain and maintain clearance

Level: Junior / Journeyman / Senior

Design, build, and operate AI/ML solutions on the Advana data and analytics platform, including traditional ML and Generative AI/LLM capabilities. Work closely with data engineers, software engineers, and mission stakeholders to turn data into deployed, monitored models that support DoD decision-making.

• Develop, train, and evaluate ML models (classification, regression, clustering, NLP, time series)
• Build MLOps pipelines for data prep, training, deployment, and monitoring on cloud infrastructure
• Integrate models into production services and user-facing applications (APIs, microservices, dashboards)
• Implement and tune Generative AI/LLM solutions (e.g., RAG, prompt engineering, fine-tuning) using enterprise and mission data
• Collaborate with data engineers to define features, data quality checks, and scalable data pipelines
• Monitor model performance and drift; design retraining strategies and A/B tests
• Document models, assumptions, and limitations; support DoD Responsible/Ethical AI requirements

• Strong Python development (pandas, Num Py, scikit-learn; plus Tensor Flow and/or PyTorch)
• Hands‑on experience building and deploying ML models end‑to‑end (from data exploration to production)
• Practical experience with MLOps tools and patterns (e.g., MLflow, Sage Maker, Kubeflow, or similar)
• Solid understanding of statistics, ML fundamentals, and evaluation metrics
• Experience working with cloud services (preferably AWS: S3, Lambda, ECR, ECS/EKS, Sage Maker or equivalents)
• Proficient with SQL and working with large structured/unstructured datasets
• Ability to work with cross‑functional teams (data, software, product, mission)

• Experience with LLMs / Generative AI, RAG architectures, and vector databases
• Experience on large‑scale data platforms (Databricks, Spark) and event/stream processing
• Experience in DoD, Federal, or other highly regulated environments (security, compliance, RMF awareness)
• Familiarity with CI/CD, containerization (Docker), and Kubernetes for model deployment
• Relevant certifications (e.g., AWS Machine Learning Specialty, Databricks, or similar)