Technology

AI/ML Engineering Lead- W2 | Onsite Interview

Blue Space TechnologiesLinthicum Heights, MD🇺🇸United StatesPosted Oct 22, 2026

Quick Overview

Seniority

Mid Senior

Work mode

On Site

Location

Linthicum Heights, MD, United States

Posted

23 hours ago

DockerAWSMLOpsMLflowScikit-learnAirflowApacheGoogle CloudHugging FacePulumiPyTorchPythonTensorFlowTerraformGenerative AIAzureKubernetesPrompt EngineeringCI/CDRAG

Job Description

AI/ML Engineering Lead- W2 | Onsite Interview
Location: Linthicum Heights, MD

Position 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.
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.