Platform Engineer

This is a fully remote position, open to applicants in Illinois.

📋 Description

• Design and execute the MLOps strategy for the program, ensuring alignment with the project proposal and delivery roadmap.

• Develop and manage enterprise-level ML/LLM pipelines that encompass model training, validation, deployment, versioning, monitoring, and CI/CD automation utilizing GCP-native services.

• Create container-oriented ML platforms with a GKE-first approach while assessing orchestration tools such as Kubeflow, Vertex AI, MLflow, Airflow, and others.

• Establish hybrid MLOps and LLMOps workflows, which include prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems in GCP.

• Act as a technical authority across internal and client projects, contributing to architectural patterns, best practices, and reusable GCP frameworks.

• Facilitate observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems.

• Collaborate with data engineering, platform, DevOps, and client stakeholders to deliver production-ready ML solutions on Google Cloud.

• Ensure that solutions comply with security, governance, and compliance requirements.

• Conduct architecture reviews, troubleshoot complex ML system challenges, and guide implementation across cloud-native ML platforms on GCP.

• Mentor engineers and provide insights on modern MLOps tools, Vertex AI capabilities, and best practices.

⛳️ Requirements

• Over 10 years of experience in ML/AI platform engineering or AI/MLOps roles with a strong focus on architecture.

• In-depth expertise in the Google Cloud (GCP) native AI/ML stack, encompassing Vertex AI, Google Kubernetes Engine (GKE), Cloud Functions, AutoML, Vertex AI Pipelines, BigQuery ML, API Gateway, and CI/CD (Cloud Build/Cloud Deploy or equivalent).

• Practical experience with MLOps tools such as MLflow, Kubeflow, Vertex AI Pipelines, Airflow, BentoML, KServe, and Seldon.

• Profound knowledge of model lifecycle management.

• Experience in implementing or supporting LLMOps pipelines, including prompt versioning, evaluation metrics, and automation frameworks.

• Comprehensive understanding of the ML lifecycle, which includes data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.

• Extensive experience with Google Cloud's Vertex AI platform, including Pipelines, Feature Store, Model Registry, and Model Monitoring.

• Proven experience in implementing ML CI/CD pipelines.

• Strong SQL skills and data transformation experience using Snowflake, Databricks, and Spark.

• Experience with feature engineering pipelines and managing Feature Store.

• Knowledge of lineage tracking, metadata tracking, and reproducibility.

• Hands-on experience with Vertex AI Foundation Models, OpenAI, Anthropic, or Llama models.

• Familiarity with Cloud Monitoring, Vertex AI Model Monitoring, Prometheus, and Grafana.

• Strong foundational skills in Python and cloud-native development patterns.

• Solid understanding of security best practices, Cloud IAM, secrets management, and artifact governance.

• Work location requirement: Chicago, IL or nearby states.

• Willing to travel up to 30%.

🏝️ Benefits

• Opportunity to engage with Fortune 500 companies and innovative market disruptors.

• Access to cutting-edge technologies related to artificial intelligence, machine learning, data, and cloud.

• Chance to be part of an AI-first digital transformation and engineering organization.

• Leadership role within a dynamic team.

• Travel opportunities of up to 30%.