Artificial Intelligence & Machine Learning Architect SME

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Artificial Intelligence & Machine Learning Architect SME based in United States.

This is a senior-level opportunity to shape the AI and machine learning architecture behind a major enterprise application modernization initiative.
You will define scalable, secure, and modular architectures that support complex cloud-based ML workloads.
The role spans the full ML lifecycle, from data ingestion and model development through deployment, monitoring, and retraining.
You will work within an agile environment alongside engineering and technical teams to turn advanced AI capabilities into reliable mission outcomes.
Your expertise will help establish MLOps practices, reusable AI services, and highly available multi-tenant solutions.
The position also offers the opportunity to influence technology decisions, responsible AI practices, and cloud infrastructure strategy.
With a fully remote setup and limited travel, this role is designed for an experienced technical leader ready to make a meaningful impact.

• 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.

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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