AI/ML Engineering Lead

Xprotege Institute of Technology and Management; Linthicum (MD); On-site; USD 180,000 - 240,000; Full time

Job summary

Xprotege Institute of Technology and Management is seeking an AI/ML Engineering Lead to own the architecture, development, implementation, and governance of a shared enterprise AI/ML platform across multiple state agencies. The role emphasizes ML engineering, MLOps, cloud infrastructure, data engineering, and Generative AI to deliver secure, scalable, reliable, and responsible AI/ML solutions.

You will lead technical direction, mentor teams, drive secure software delivery, and ensure governance,

Qualifications

• 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.

Skills

Python

PyTorch

TensorFlow

scikit-learn

Hugging Face

MLflow

Kubeflow

Apache Airflow

AWS SageMaker

Docker

Kubernetes

CI/CD pipelines

Model registries

Experiment tracking

Automation testing

Deployment

Monitoring

LLMs

Prompt engineering

RAG architecture

Model evaluation

Fine-tuning

Data pipelines

Feature stores

Data governance

Terraform

Pulumi

GitOps

Security

Responsible AI

Education

Bachelor's degree

Master's degree

Tools

Docker

Kubernetes

Terraform

Pulumi

GitOps

Job description

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 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.