JOB SUMMARY

We are seeking an AI & Data Engineer to develop scalable AI-powered automation systems that optimize decision-making, drive efficiency, and enhance operational intelligence. This role involves LLM integration, AI pipeline optimization, and secure AI model deployments, primarily on Google Cloud. You will also work with data infrastructure on Azure and AWS, ensuring seamless AI and data interoperability across cloud environments. The position requires a strong focus on AI governance, security, and real-time AI-driven decision-making.

Objectives of this role

• Build AI-driven automation systems to enhance decision-making, optimize workflows, and improve operational efficiency.
• Ensure AI-driven services comply with security, privacy, and regulatory standards, including PCI-DSS, GDPR, and responsible AI principles.
• Develop scalable AI models using Google Cloud’s Vertex AI, while ensuring integration with data infrastructure in Azure and AWS.
• Lead the implementation of MLOps best practices to streamline AI lifecycle management across cloud environments.
• Collaborate with IT, Compliance, Engineering, and Business teams to integrate AI seamlessly into company-wide systems.
• Champion responsible AI practices, ensuring fairness, transparency, and bias mitigation in AI models.

MINIMUM QUALIFICATIONS

• 6-8 years of professional data engineering experience
• Expertise in AI model development (LLMs, NLP, recommendation systems).
• Strong background in MLOps, including CI/CD for AI models and continuous learning pipelines.
• Hands-on experience deploying AI workloads on Google Cloud (Vertex AI, BigQuery, Kubernetes, Dataflow) while integrating with Azure and AWS for data processing.
• Proficiency in Python & SQL for AI-driven data processing and automation.
• Strong knowledge of cloud security frameworks, encryption techniques, and compliance for AI systems.
• Verano requires applicants to have permanent United States work authorization (U.S. citizen or Permanent Resident).
• This hybrid role requires 3 days a week onsite at our offices in Chicago, IL.

PREFERRED QUALIFICATIONS

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field.
• Experience developing AI-powered automation solutions with a Google Cloud-first approach while maintaining interoperability with Azure and AWS.
• Deep knowledge of AI ethics, responsible AI development, and model interpretability.
• Hands-on experience with cloud-native AI deployment, real-time inference scaling, and hybrid-cloud AI solutions.

ESSENTIAL DUTIES AND RESPONSIBILITIES

AI & LLM Integration

• Deploy and fine-tune LLMs for AI-powered automation, intelligent decision-making, and real-time customer interactions.
• Optimize retrieval-augmented generation (RAG) pipelines for low-latency AI responses, leveraging BigQuery embeddings and Cloud AI Search.
• Enhance AI-driven personalization and recommendation engines through vector search and real-time adaptation models.

Data Engineering & ETL Pipelines

• Design and optimize AI-driven ETL workflows for real-time and batch data processing using BigQuery (Google Cloud), Azure Data Factory, and AWS Glue.
• Implement event-driven architectures for AI applications using Cloud Pub/Sub, Azure Event Hubs, and AWS Kinesis.
• Build data interoperability layers that allow AI systems to seamlessly integrate with data lakes and warehouses across GCP, Azure, and AWS.

MLOps & AI Lifecycle Automation

• Implement Vertex AI Pipelines and Google Cloud Build for AI model deployment, monitoring, and retraining.
• Develop multi-cloud CI/CD pipelines for AI models running on GCP, with data sources from Azure and AWS.
• Ensure continuous AI model retraining to address model drift and improve AI decision-making.

Backend & Microservices Development

• Develop AI-powered APIs and microservices using FastAPI, Flask, and Django, deploying serverless inference models with Cloud Run, Azure Functions, and AWS Lambda.
• Implement containerized AI applications with GKE, AKS, and EKS for scalable and efficient deployment.

Cloud & Infrastructure Optimization

• Optimize AI infrastructure for scalability, cost-efficiency, and high availability across GCP, Azure, and AWS while ensuring cross-cloud compatibility.
• Implement RBAC and IAM policies to secure AI and data-intensive workloads using Vertex AI, BigQuery, and other cloud-native services.

Security & Compliance

• Ensure AI compliance with PCI-DSS, GDPR, HIPAA, and industry regulations while securing models with Google and Azure Confidential Computing.
• Implement differential privacy and bias-mitigation frameworks for secure, fair, and explainable AI-driven personalization.

Objectives of this role

• Build AI-driven automation systems to enhance decision-making, optimize workflows, and improve operational efficiency.
• Ensure AI-driven services comply with security, privacy, and regulatory standards, including PCI-DSS, GDPR, and responsible AI principles.
• Develop scalable AI models using Google Cloud’s Vertex AI, while ensuring integration with data infrastructure in Azure and AWS.
• Lead the implementation of MLOps best practices to streamline AI lifecycle management across cloud environments.
• Collaborate with IT, Compliance, Engineering, and Business teams to integrate AI seamlessly into company-wide systems.
• Champion responsible AI practices, ensuring fairness, transparency, and bias mitigation in AI models