Lead Data Engineer - GCP AI Architect

Capgemini • Chicago, ND

1d • $144,890 - $190,117 • Hybrid

About The Position

We are seeking an experienced GCP AI Architect to lead the design, architecture, and implementation of enterprise-scale Artificial Intelligence (AI), Generative AI, Machine Learning (ML), and Data Analytics solutions on Google Cloud Platform (GCP). The ideal candidate will combine deep expertise in cloud architecture, AI/ML technologies, and business transformation to create scalable, secure, and responsible AI solutions that deliver measurable business value.

Requirements

• 8+ years of experience in cloud architecture, software engineering, or data platforms.
• 3+ years of experience designing AI/ML solutions on GCP.
• Experience leading enterprise-scale cloud transformation programs.
• Experience architecting Generative AI and LLM-based applications.
• Machine Learning model lifecycle
• Natural Language Processing (NLP)
• Computer Vision
• Generative AI
• Large Language Models (LLMs)
• Vertex AI Model Garden
• Prompt Engineering
• Fine-tuning Foundation Models
• Vector Databases and Embeddings
• RAG Architecture
• AI Agents and Multi-Agent Systems

Nice To Haves

• Google Cloud Professional Cloud Architect
• Google Cloud Professional Machine Learning Engineer
• Google Cloud Professional Data Engineer

Responsibilities

• Design end-to-end AI, Generative AI, and ML solution architectures on GCP.
• Define enterprise AI strategies, roadmaps, and reference architectures.
• Architect Retrieval-Augmented Generation (RAG), Agentic AI, and AI-powered business applications.
• Evaluate and select appropriate Foundation Models, Gemini models, and open-source LLMs.
• Design prompt engineering, fine-tuning, grounding, and vector search strategies.
• Design scalable, secure, and highly available cloud-native architectures using Vertex AI, BigQuery, Cloud Storage, Cloud Run, Google Kubernetes Engine (GKE), Dataflow, Pub/Sub, Cloud Functions.
• Define multi-region and disaster recovery strategies.
• Ensure architectural alignment with enterprise cloud standards.
• Establish enterprise MLOps and LLMOps frameworks.
• Design automated model development, deployment, monitoring, and governance pipelines.
• Define model lifecycle management processes.
• Implement CI/CD practices for AI solutions.
• Monitor performance, model drift, and operational efficiency.
• Design modern data architecture supporting AI initiatives.
• Create data lakes, lakehouses, and enterprise analytics solutions.
• Establish feature engineering and data preparation frameworks.
• Define data quality, governance, lineage, and metadata management practices.
• Define AI governance frameworks and policies.
• Implement data privacy, compliance, and security controls.
• Ensure Responsible AI principles including Fairness, Transparency, Explainability, Bias detection, Risk mitigation.
• Collaborate with security and compliance teams.

Benefits

• Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade
• Company paid holidays
• Personal Days
• Sick Leave
• Medical, dental, and vision coverage
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
• Life and disability insurance
• Employee assistance programs

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed