Senior 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 Senior AI/ML Platform Engineer will design, implement, operate, and continuously improve the state's shared enterprise AI/ML platform. The role focuses on platform automation, MLOps, CI/CD, cloud infrastructure, observability, reliability engineering, and cost optimization. The engineer will own critical platform components end-to-end, improve developer productivity and deployment efficiency, establish engineering standards, and mentor other engineers while enabling secure, reliable, and scalable AI/ML services across multiple state agencies.

• Pipeline Automation and CI/CD:
  Design and maintain enterprise-grade ML CI/CD pipelines for automated model validation, testing, A/B testing, canary deployments, rollback, and production releases. Implement Git Ops practices and improve model-to-production delivery times.
• Observability and Reliability:
  Establish monitoring, alerting, and service-level objectives (SLOs) for platform services and deployed models. Implement model monitoring for data drift, concept drift, and performance degradation. Lead incident response, root-cause analysis, and corrective actions.
• Cloud Cost Optimization:
  Monitor cloud expenditure, identify resource inefficiencies, and optimize GPU/CPU utilization, autoscaling, workload scheduling, and infrastructure sizing. Evaluate platform components based on performance and total cost of ownership.
• Developer Experience and Self-Service:
  Develop internal SDKs, CLI tools, reusable templates, APIs, and standardized deployment workflows. Maintain API versioning, backward compatibility, documentation, runbooks, architecture decision records (ADRs), and onboarding guides.
• Platform Engineering and Operations:
  Own the architecture, implementation, maintenance, and production support of core platform components. Ensure scalability, availability, security, maintainability, and operational efficiency.
• Technical Leadership and Mentorship:
  Conduct code and design reviews, mentor engineers, establish engineering best practices, participate in architecture reviews and technical evaluations, and collaborate with agency teams and platform leadership.

• Bachelor's degree from an accredited college or university in Computer Science, Information Systems, Engineering, Business, or another related scientific or technical discipline. A master's degree is preferred.
• At least ten (10) years of experience in platform engineering, MLOps, or data/software engineering, including at least two (2) years working with production ML systems.
• Strong proficiency in Python and experience developing and maintaining reliable, production-grade software.
• Demonstrated experience designing and improving ML CI/CD pipelines, automation frameworks, and deployment processes.
• Experience with AWS, Azure, or GCP, including cloud resource management and cost optimization.
• Advanced experience with Kubernetes, including cluster management, resource quotas, autoscaling, and workload scheduling.
• Hands-on experience with observability tools such as Prometheus, Grafana, Datadog, and Open Telemetry, or equivalent technologies.
• Production-scale experience with Terraform and Ansible for infrastructure automation and configuration management.
• Strong understanding of the ML lifecycle, including experiment tracking, model registries, feature stores, model serving, and drift monitoring.

• Experience in state government, the public sector, or highly regulated environments.
• Familiarity with FedRAMP, NIST AI Risk Management Framework (AI RMF), and applicable cybersecurity and privacy requirements, including CJIS, HIPAA, IRS Publication 1075, and PII protection, where applicable.
• Experience with responsible AI, model explainability, fairness…