AI​/ML Platform Engineer

At 4A Consulting, we turn complexity into opportunity. Founded in 2014 and headquartered in Baltimore, MD, we are a women-owned, boutique consulting firm specializing in delivering innovative, data-driven solutions to both the Federal Government and Fortune 500 clients. Our team blends deep industry knowledge with advanced technologies to design tailored strategies that drive measurable and sustainable outcomes. We pride ourselves on an agile, collaborative approach that helps organizations navigate challenges and seize emerging opportunities in a rapidly evolving digital landscape.

At 4A, we don’t just deliver projects, we build trusted partnerships that empower our clients to lead with confidence in the digital age.

The AI/ML Platform Engineer will develop, maintain, and optimize the infrastructure, tools, and automated workflows supporting the state's shared enterprise AI/ML platform. The role focuses on ML pipeline automation, CI/CD, model deployment, monitoring, cloud cost optimization, and developer enablement. The engineer will collaborate with data scientists, data engineers, software developers, agency stakeholders, and external partners to deliver reliable, scalable, and production-ready AI/ML solutions across state agencies.

• ML Pipeline Automation and CI/CD:
  Build and maintain automated ML pipelines for data preprocessing, feature engineering, model training, evaluation, and deployment. Implement automated testing, pipeline validation, staged deployment, and failure recovery using Apache Airflow, Kubeflow Pipelines, Prefect, or equivalent tools.
• Model Development and Deployment:
  Prototype, implement, and optimize ML algorithms and integrate models into production systems. Apply appropriate supervised and unsupervised learning, deep learning, NLP, computer vision, and Generative AI techniques based on business requirements.
• Monitoring and Reliability:
  Implement logging, metrics, dashboards, and alerts to monitor pipeline health, model performance, data drift, model degradation, and infrastructure utilization. Participate in production support, incident response, troubleshooting, and root-cause analysis.
• Cloud Cost Optimization:
  Monitor compute, storage, and data-transfer costs. Optimize resource utilization through autoscaling, spot or preemptible instances, and workload scheduling. Contribute to infrastructure-as-code configurations, resource tagging, and budget controls.
• Developer
  
  Experience:
  
  Maintain internal SDKs, self-service tools, onboarding documentation, and reusable templates. Support platform users, troubleshoot issues, resolve bugs, and conduct technical enablement sessions for agency teams.
• Collaboration and Continuous Improvement:
  Translate business requirements into technical solutions, collaborate across multidisciplinary teams, evaluate emerging ML technologies, and improve platform reliability, scalability, usability, and operational efficiency.

• Bachelor's degree in Computer Science, Engineering, or a related technical field. A master's degree or PhD is preferred but not required.
• At least five (5) years of experience in software engineering, data engineering, or ML engineering, including at least two (2) years working with production ML systems.
• Strong proficiency in Python and experience developing maintainable, production-quality code.
• Solid understanding of ML and deep learning fundamentals, including model training, evaluation, feature engineering, versioning, and deployment.
• Experience with ML frameworks and libraries such as PyTorch, Tensor Flow, or scikit-learn, and with at least one ML orchestration tool:
  Apache Airflow, Kubeflow Pipelines, or Prefect.
• Familiarity with AWS, Azure, or GCP and…