AI/ML Engineer SME

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI/ML Engineer SME based in the United States. The AI/ML Engineer SME will serve as a senior technical authority responsible for shaping scalable AI and machine learning architecture within a complex modernization program.

You’ll design and support end-to-end ML pipelines spanning data ingestion, feature engineering, model training, deployment, monitoring, and retraining.

The role combines hands-on engineering with architectural leadership across cloud-native, distributed, and multi-tenant environments.

You’ll help embed MLOps, Infrastructure-as-Code, security, responsible AI, and automation into enterprise machine learning solutions.

Working within an Agile development environment, you’ll collaborate with technical teams and guide decisions around ML platforms, tools, frameworks, and services.

Your expertise will directly contribute to highly available, secure, performant, and cost-efficient AI/ML capabilities supporting mission-critical applications.

This is a remote opportunity for an experienced AI/ML professional seeking meaningful technical ownership and the chance to influence large-scale modernization efforts.

Accountabilities

• Define, document, and maintain scalable, modular AI/ML architectures aligned with enterprise cloud strategies and product requirements.
• Architect and implement end-to-end machine learning pipelines covering data ingestion, feature engineering, model training, deployment, monitoring, and retraining.
• Apply MLOps best practices to enable continuous integration, delivery, deployment, and lifecycle management of ML models.
• Design multi-tenant and multi-region AI/ML workloads that are elastic, highly available, secure, and cost-efficient.
• Use Infrastructure-as-Code tools to provision and manage cloud-based AI/ML infrastructure in accordance with applicable federal security and compliance standards.
• Design and support reusable, secure, and high-performance AI/ML services and APIs for integration with enterprise applications.
• Conduct model validation, optimization, and performance assessments in cloud environments while promoting responsible AI, fairness, and transparency.
• Maintain comprehensive AI/ML architecture documentation and update technical artifacts throughout Agile sprint cycles and as solutions evolve.
• Incorporate AI/ML metrics, resource utilization measures, and performance KPIs into technical dashboards and reporting.
• Guide development teams in evaluating and securely integrating third-party ML tools, frameworks, platforms, and SaaS solutions.
• Collaborate with Agile development teams and technical stakeholders to support enterprise application modernization initiatives.

Requirements

• 15+ years of specialized experience in information systems or a related technical discipline, with substantial experience in AI/ML engineering and architecture.
• Bachelor’s degree or equivalent required; a master’s degree in a related field is preferred. Equivalent professional experience may be considered in lieu of a degree.
• Demonstrated success architecting and deploying machine learning workflows in cloud environments such as AWS SageMaker, Azure Machine Learning, or Google Cloud Vertex AI.
• Hands-on experience with machine learning and deep learning frameworks including TensorFlow, PyTorch, scikit-learn, XGBoost, or Keras.
• Strong Python programming skills and experience using Docker and Kubernetes for machine learning workloads.
• Experience with MLOps technologies such as MLflow, Kubeflow, TFX, or Airflow and integrating them with CI/CD workflows.
• Familiarity with federal data governance, security, and privacy standards, including JISF, NIST 800-53, and FedRAMP.
• Proficiency with Infrastructure-as-Code tools such as Terraform, AWS CDK, or CloudFormation.
• Experience designing and supporting multi-tenant, distributed, and cloud-native systems.
• Strong architectural, analytical, problem-solving, and technical communication skills.
• Ability to work effectively within Agile development environments and provide technical guidance to cross-functional teams.
• Relevant certifications such as AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, or equivalent are preferred but not required.
• Must be able to successfully complete a background i…