Architect - Platform Engineering - USA

AI Platform/AIOps Architect

Experience Level

10+ Years

Location

• Chicago, IL or from Surrounding States
• Key Skills
• 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, Auto ML, Vertex AI Pipelines, Big Query 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, Bento ML, 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: 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, Open AI, 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.

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, Dev Ops, 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 - up to 30%
• What is in it for you

Be part of the fastest‑growing AI‑first digital transformation and engineering company in the world

• Be a leader of an energetic team of highly dynamic and talented individuals
• Exposure to working with fortune 500 companies and innovative market disruptors

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

#J-18808-Ljbffr