Ai Ml Engineering Lead

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 GCP.

• 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.

• FedRAMP, 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.