Artificial Intelligence & Machine Learning Architect SME

Accountabilities:

• Define, document, and maintain scalable, modular AI/ML architectures aligned with enterprise cloud strategies, product requirements, security standards, and modernization objectives.
• Architect end-to-end machine learning pipelines covering data ingestion, feature engineering, model training, deployment, monitoring, optimization, and retraining.
• Establish and apply MLOps best practices to support continuous integration, delivery, deployment, and lifecycle management of machine learning models.
• Design multi-tenant and multi-region AI/ML workloads that are elastic, highly available, secure, performant, and cost-efficient.
• Use Infrastructure-as-Code practices to provision and manage cloud-based AI/ML infrastructure in accordance with federal compliance and security requirements.
• Design and support reusable AI/ML services and APIs for integration with enterprise applications, with a strong focus on security, reliability, and performance.
• Conduct model validation and optimization while promoting responsible AI principles, including fairness, transparency, and appropriate governance.
• Partner with engineering and technical teams to select and integrate third-party ML tools, frameworks, and SaaS solutions in a secure and compliant manner.
• Maintain architecture documentation and technical artifacts throughout sprint cycles, ensuring they remain accurate as solutions evolve.
• Define and monitor AI/ML metrics, resource utilization measures, and performance KPIs for technical dashboards and reporting.
• Provide technical guidance and subject-matter expertise to teams working across cloud, data, software, and machine learning disciplines.

Requirements:

• Bachelor's degree or equivalent required in a relevant field; a master's degree in a related discipline is preferred. Equivalent professional experience may be considered in place of a degree.
• 15+ years of specialized information systems experience, or an equivalent combination of education and professional experience.
• Demonstrated experience architecting and deploying machine learning workflows in cloud environments such as AWS SageMaker, Azure Machine Learning, or Google Vertex AI.
• Strong hands-on knowledge of ML/DL frameworks including TensorFlow, PyTorch, scikit-learn, XGBoost, or Keras.
• Advanced Python programming skills, along with experience using Docker and Kubernetes for containerized and orchestrated ML workloads.
• Practical experience with MLOps technologies such as MLflow, Kubeflow, TFX, or Airflow and integrating them into CI/CD workflows.
• Proficiency with Infrastructure-as-Code tools such as Terraform, AWS CDK, or CloudFormation.
• Experience designing multi-tenant distributed systems and cloud-native architectures.
• Familiarity with federal data governance, security, and privacy frameworks, including NIST 800-53 and FedRAMP.
• Strong analytical, architectural, and problem-solving capabilities, with the ability to translate complex technical requirements into scalable solutions.
• Relevant professional certifications, such as AWS Certified Machine Learning – Specialty or Google Professional Machine Learning Engineer, are preferred but not required.
• Must be able to pass a background check to obtain a Public Trust position.
• Must be a U.S. Person, including a U.S. citizen, lawful permanent resident, refugee, or asylee.
• Comfortable working remotely within an agile environment, with strong communication and collaboration skills across distributed teams.

Benefits:

• Competitive compensation: Estimated salary range of $195,240–$264,148 annually, depending on experience, geographic location, and contractual requirements.
• Remote work: Fully remote position with full-time hours of approximately 40 hours per week.
• Flexible work environment: Full-flex work weeks where operationally feasible.
• Healthcare: Multiple medical plan options, including plans with Health Savings Accounts, plus dental and vision coverage.
• Retirement: 401(k) plan with company matching and pre- and post-tax contribution options.
• Paid time off: Vacation, sick, personal, holiday, parental, military, bereavement, and jury-duty leave.
• Paid holidays: Typically 10 paid holidays annually, in addition to paid leave.
• Paid family leave: Up to 160 hours of paid family leave during a rolling 12-month period for eligible employees.
• Insurance protection: Short- and long-term disability, life insurance, accidental death and dismemberment, personal accident, critical illness, and business travel and accident coverage.
• Career development: AI-powered career tools, learning opportunities, professional growth resources, and internal mobility support.
• Work-life balance: Programs and benefits designed to support personal well-being and sustainable career growth.
• Travel: Less than 10% travel expected.