AI/ML Engineering Lead

• W2 | Onsite Interview
  Skills
• Artificial Intelligence
• Machine Learning Operations (ML Ops)
• Artificial Intelligence
• Machine Learning Operations (ML Ops
• -SummaryAI/ML Engineering Lead
• W2 |

Onsite InterviewLocation:

Linthicum Heights, MDPosition Overview:

The AI/ML Engineering Lead will lead the architecture, development, implementation, and governance of a shared enterprise AI/ML platform supporting multiple state agencies. This role focuses on ML engineering, MLOps, cloud infrastructure, data engineering, and Generative AI to deliver secure, scalable, reliable, and responsible AI/ML solutions.

Key Requirements:

• 16+ years of software or data engineering experience, including 3+ years focused on production ML systems, MLOps, or ML platform engineering.
• Bachelor's degree in Computer Science, Data Science, Engineering, IT, or a related field; master's degree preferred.
• Experience building or operating shared enterprise AI/ML platforms supporting multiple teams or agencies.
• Strong proficiency in Python and experience with PyTorch, TensorFlow, scikit-learn, and Hugging Face.
• Hands-on experience with MLflow, Kubeflow, Apache Airflow, or equivalent MLOps tools.
• Experience with AWS SageMaker or equivalent AI/ML services on Azure or Google Cloud Platform.
• Experience with Docker, Kubernetes, CI/CD pipelines, model registries, experiment tracking, automated testing, deployment, and monitoring.
• Strong understanding of LLMs, prompt engineering, RAG architecture, model evaluation, and fine-tuning.
• Experience with data pipelines, feature stores, data quality, data lineage, and governance.
• Experience with Terraform or Pulumi and GitOps practices.
• Strong technical leadership, architecture, mentoring, security, and responsible AI experience.

Preferred Experience:

• State government, public sector, or highly regulated environments.
• Fed

RAMP, NIST AI

Risk Management Framework, and applicable cybersecurity and privacy requirements.

• Responsible AI, model explainability, fairness auditing, bias mitigation, and model risk management.
• Platform observability, reliability engineering, incident management, disaster recovery, and cloud cost optimization.

Employers have access to artificial intelligence language tools ("AI") that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.