Senior Consultant, AI/ML Engineer

About the role

We are seeking a Senior Consultant, AI/ML Engineer to help operationalize machine learning across a dynamic, mission-driven environment. In this hands-on role, you will build and support the infrastructure, pipelines, and production practices that move models from experimentation to reliable real-world use.

Responsibilities

• Own the end-to-end machine learning lifecycle, including training pipelines, model registry, versioning, promotion, and deployment workflows.
• Design and maintain SageMaker-based training and inference platforms that support production-grade ML services.
• Develop Infrastructure as Code using Terraform for AWS services such as SageMaker, S3, KMS, IAM, CloudWatch, and cross-account access patterns.
• Build and improve CI/CD pipelines to support testing, security scanning, infrastructure validation, and automated model promotion.
• Ensure consistency between training and serving by maintaining shared feature engineering and preprocessing logic.
• Set up monitoring, alerting, and dashboarding for model performance, drift, endpoint health, and operational visibility.
• Support secure and scalable ML operations through least-privilege access, encryption, secrets management, and artifact protection.
• Collaborate closely with data scientists to turn experimental models into tested, reproducible, deployable solutions.
• Support Bedrock-based workflows for batch and real-time use cases, with attention to throughput, cost, and guardrails.
• Help improve incident response and operational efficiency through automation, anomaly detection, and AI-assisted analysis.

Skills

• 5+ years of experience in MLOps, ML platform engineering, or ML infrastructure roles with production ownership of deployed models.
• Strong AWS expertise, especially with SageMaker, S3, IAM, KMS, CloudWatch, Lambda, Step Functions, and multi-account environments.
• Hands-on experience with Terraform and Git-based DevOps workflows.
• Experience designing and maintaining CI/CD pipelines using tools such as GitLab CI, GitHub Actions, or similar platforms.
• Advanced Python development skills for building reliable, production-ready ML tooling and services.
• Solid understanding of model training, evaluation, feature engineering, and metrics such as C-index, AUC, and calibration.
• Experience with model monitoring, drift detection, and production ML support.
• Strong knowledge of security best practices, including least-privilege access, encryption, and secrets management.
• Ability to work cross-functionally and translate technical needs into practical, scalable solutions.

Preferred Skills

• Experience supporting LLM pipelines and Bedrock-based implementations.
• Familiarity with blue/green deployment strategies and automated rollback processes.
• Knowledge of infrastructure and application security tools such as Checkov and SonarQube.
• Exposure to anomaly detection, event-driven automation, and AIOps practices.
• Experience using AI tools to support log analysis, incident triage, or root-cause investigation.
• Background working in highly regulated or operationally sensitive environments.

Pay

The pay range for this role is $69 - $107 per hour based on qualifications and experience.

Benefits

• Competitive compensation and benefits including medical, dental, vision, and retirement.