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# AI Platform Architect in Chicago

- **Company:** Energy Jobline
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Airflow, Architectural Patterns, Audit Trail, BigQuery, Cloud Computing, Continuous Integration, Information Engineering, Data Transformation, DevOps, Monitoring of Systems, Machine Learning, OpenAI, Prometheus, Standard Sql, Azure Machine Learning, Software Engineering, Management of Software Versions, Feature Store, Google Cloud, Feature Engineering, Data Ingestion, Cloud Monitoring, Large Language Models, Snowflake, Grafana, Apache Spark, Generative AI, Build Server, AutoML, AI Platforms, Kubernetes, Data Lineage, Machine Learning Operations, Drift Detection, Model Registry, Api Gateway, AIOps, Software Version Control, KServe, Databricks
- **Published:** October 31, 2026
- **Apply:** https://www.energyjobline.com/job/ai-platform-architect-chicago-32123196

## About the Role

We are seeking a senior, visionary AI Platform / AIOps Architect with 10+ years of deep technical and architectural experience to lead the design, evolution, and scaling of our enterprise AI/ML platforms. In this role, you will be the primary technical authority for our machine learning and generative AI infrastructure, bridging the gap between cutting-edge data science, cloud- infrastructure, and enterprise-grade software engineering., * 10+ years working in ML/AI platform engineering or AI/MLOps roles with strong architecture exposure.
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 * Strong expertise in the Google Cloud (GCP) 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).
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 * Hands-on experience with MLOps toolset and awareness of: MLflow, Kubeflow, Vertex AI Pipelines, Airflow, BentoML, KServe, Seldon.
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 * Deep understanding of model lifecycle management (feature engineering -> training -> registry -> deployment -> monitoring).
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 * Experience implementing or supporting LLMOps pipelines, including prompt versioning, evaluation metrics, and automation frameworks.
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 * Deep understanding of the ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.
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 * Strong experience with Google Cloud's Vertex AI platform, including Pipelines, Feature Store, Model Registry, and Model Monitoring.
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 * Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
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 * Strong SQL and data transformation experience using Snowflake, Databricks, Spark.
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 * Experience with feature engineering pipelines and Feature Store management.
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 * Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, and reproducibility.
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 * Hands-on experience with Vertex AI Foundation Models, OpenAI, Anthropic, or Llama models.
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 * Experience with Cloud Monitoring, Vertex AI Model Monitoring, Prometheus/Grafana.

## Description

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