Machine Learning Engineer (AI)

TL;DR

Machine Learning Engineer (AI/ML): Oversee AI/ML strategy, architecture, and implementation for government products with an accent on scalable models, predictive analytics, automation frameworks, and secure integration. Focus on designing MLOps pipelines, model validation, bias mitigation, and transitioning prototypes to production systems.

Hybrid in Columbia, MD (Eastern Time Zone, 9 AM–5 PM ET). Must pass U.S. public trust clearance: U.S. citizen or 3 out of last 5 years U.S. residency with valid passport and visa/work permit. Occasional travel (<5%).

$104,900 - $150,000

Company

Digital services company partnering with U.S. government agencies to build intuitive products for veterans, service members, families, and seniors.

What you will do

• Serve as technical authority for AI/ML strategy, architecture, and implementation.
• Design and deploy scalable AI/ML models and lead predictive analytics, automation, and decision-support systems.
• Integrate AI solutions into secure enterprise environments and ensure data governance compliance.
• Oversee data engineering pipelines, MLOps, model validation, explainability, and bias mitigation.
• Advise leadership and stakeholders on AI technologies and support prototype transitions to production.
• Mentor junior engineers and provide technical leadership across cross-functional teams.

Requirements

• All candidates must pass U.S. public trust clearance (U.S. citizen or 3/5 years U.S. residency, valid passport, visa/work permit).
• Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or related field.
• 4+ years in software engineering, data science, or AI/ML development.
• Experience with Python/R, ML frameworks, data engineering/large-scale processing, model deployment, and MLOps.
• AI solutions in secure/regulated environments.

Nice to have

• ML fundamentals, deep learning (CNNs, RNNs, transformers, LLMs), evaluation, tuning, explainability, bias mitigation.
• Python ML libraries (TensorFlow, PyTorch, Scikit-learn), LLM tools (LangChain, vector DBs, RAG), MLOps platforms (Azure ML, AWS SageMaker, MLflow).
• Data processing/feature engineering, large datasets, ETL.
• Translating business problems to AI solutions, leading initiatives, cross-functional collaboration.
• Governance, ethics, responsible AI, privacy laws (GDPR, CCPA), risk assessments.

Culture & Benefits

• Hybrid work environment in Eastern Time Zone.
• Competitive salary and full healthcare benefits.
• Equal opportunity employer.