AI/ML Engineering Lead

Creative Global Consulting; Linthicum (MD); On-site; USD 180,000 - 260,000; Full time

Job summary

Creative Global Consulting is seeking an AI/ML Engineering Lead to architect, develop and govern a shared enterprise AI/ML platform serving multiple state agencies. The role emphasizes ML engineering, MLOps, cloud infrastructure, data pipelines and Generative AI to deliver secure, scalable solutions.

Ideal candidates have 16+ years in software/data engineering (3+ in production ML), strong Python and ML framework expertise, and hands-on experience with MLflow, Kubeflow and CI/CD pipelines.

Qualifications

• 16+ years of software or data engineering experience, incl. 3+ years in production ML systems, MLOps, or ML platform engineering.
• Bachelor's degree in CS, DS, Engineering, IT, or related field; master's preferred.
• Experience building or operating shared enterprise AI/ML platforms for multiple teams/agencies.
• Strong Python proficiency and experience with ML frameworks (PyTorch, TensorFlow, scikit-learn, Hugging Face).
• Hands-on with MLflow, Kubeflow, Airflow or equivalent MLOps tools.
• Experience with AWS SageMaker or Azure/GCP ML services.
• Experience with Docker, Kubernetes, CI/CD, model registries, experimentation tracking, deployment and monitoring.
• Strong understanding of LLMs, prompt engineering, RAG, evaluation and fine-tuning.
• Experience with data pipelines, feature stores, governance and data quality.
• Experience with Terraform or Pulumi and GitOps practices.
• Strong technical leadership, architecture, mentoring, security and responsible AI experience.

Responsibilities

• Lead architecture, development, implementation and governance of a shared enterprise AI/ML platform for multiple state agencies.
• Focus on ML engineering, MLOps, cloud infra, data engineering and Generative AI to deliver secure, scalable, reliable AI/ML solutions.
• Provide technical leadership, mentoring and ensure security and responsible AI practices across programs.

Skills

Leadership

ML architecture

MLOps

Prompt engineering

Security & governance

Education

Bachelor's degree in CS/DS/Engineering

Master's degree preferred

Tools

Python

PyTorch

TensorFlow

scikit-learn

Hugging Face

MLflow

Kubeflow

Apache Airflow

Docker

Kubernetes

CI/CD

Model registries

Experiment tracking

AWS SageMaker

Azure

GCP

Terraform

Pulumi

GitOps

Job description

Job Details

Role: AI/ML Engineering Lead

Location: Linthicum Heights, MD

Duration: Long Term Contract

Work Arrangement: On-site

Interview Mode: Telephonic followed by In-Person

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 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.