AI/ML Engineering Lead
Job in
Linthicum, Anne Arundel County, Maryland, USA
Listed on 2026-11-05
Listing for:
Creative Global Consulting
Full Time
position Listed on 2026-11-05
Job specializations:
-
Software Development
AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Job Description & How to Apply Below
Role: AI/ML Engineering Lead
Location:
Linthicum Heights, MD
Duration:
Long Term Contract
Work Arrangement:
On-site
Interview Mode:
Telephonic followed by In-Person
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, Tensor Flow, scikit-learn, and Hugging Face.
- Hands-on experience with MLflow, Kubeflow, Apache Airflow, or equivalent MLOps tools.
- Experience with AWS Sage Maker 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 Git Ops practices.
- Strong technical leadership, architecture, mentoring, security, and responsible AI 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.
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