Architect - Platform Engineering - USA

Responsibilities:

• Architect and implement the MLOps strategy for the program, ensuring alignment with the project proposal and delivery roadmap.
• Design and own enterprise-grade ML/LLM pipelines covering model training, validation, deployment, versioning, monitoring, and CI/CD automation using GCP-native services.
• Build container-oriented ML platforms (GKE-first) while evaluating alternative orchestration tools with similar capabilities (Kubeflow, Vertex AI, MLflow, Airflow, etc.).
• Implement hybrid MLOps + LLMOps workflows, including prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems within the GCP environment.
• Serve as a technical authority across multiple internal and customer projects, contributing architectural patterns, best practices, and reusable frameworks for GCP.
• Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems using tools like Cloud Monitoring and Vertex AI Model Monitoring.
• Collaborate with cross-functional teams — data engineering, platform, DevOps, and client stakeholders — to deliver production-ready ML solutions on Google Cloud.
• Ensure all solutions adhere to security, governance, and compliance expectations, particularly around handling GCP services, Google Kubernetes Engine workloads, and MLOps tools.
• Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation across cloud-native ML platforms on GCP.
• Mentor engineers and provide guidance on modern MLOps tools, Vertex AI platform capabilities, and best practices.
• Travel Required - upto 30%
• What is in it for you:

Exposure to the latest technologies related to artificial intelligence and machine learning, data and cloud

Qualifications

• Level: 10+ Years
• Work location: Chicago, IL or from Surrounding States
• 10+ years working in ML/AI platform engineering or AI/MLOps roles with strong architecture exposure.
• Strong expertise in the Google Cloud (GCP) native AI/ML stack, including: Vertex AI (primary), Google Kubernetes Engine (GKE), Cloud Functions, AutoML, Vertex AI Pipelines, BigQuery ML, API Gateway, and CI/CD (Cloud Build/Cloud Deploy or equivalent).
• Hands-on experience with MLOps toolset and awareness of: MLflow, Kubeflow, Vertex AI Pipelines, Airflow, BentoML, KServe, Seldon.
• Deep understanding of model lifecycle management (feature engineering -> training -> registry -> deployment -> monitoring).
• Experience implementing or supporting LLMOps pipelines, including prompt versioning, evaluation metrics, and automation frameworks.
• Deep understanding of the ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.
• Strong experience with Google Cloud's Vertex AI platform, including Pipelines, Feature Store, Model Registry, and Model Monitoring.
• Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
• Strong SQL and data transformation experience using Snowflake, Databricks, Spark.
• Experience with feature engineering pipelines and Feature Store management.
• Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, and reproducibility.
• Hands-on experience with Vertex AI Foundation Models, OpenAI, Anthropic, or Llama models.
• Experience with Cloud Monitoring, Vertex AI Model Monitoring, Prometheus/Grafana.
• Strong foundation in Python and cloud-native development patterns.
• Solid understanding of security best practices, Cloud IAM, secrets management, and artifact governance.