Google Cloud Platform AI Architect (Glass Layer Tech Lead) - New Jersey, Atlanta, Onsite. Must have strong experinece in Python, SQL, BigQuery, Google Cloud Platform, GKE, Docker, Kubernetes, and Terraform. Preferred only locals and must attend F2F interv

Google Cloud Platform AI Architect ( Glass Layer Tech Lead)* Location - NJ/ATL, Onsite
Role Overview
We are seeking a Glass Layer Tech Lead to lead the design, development, and production readiness of the EBC backend and runtime platform. This role will own the backend services responsible for APIs, session and identity context, system integrations, and the reliable delivery of enterprise data and AI-generated responses.
The ideal candidate is a hands-on technical leader with deep backend engineering experience and strong expertise in Python, API architecture, cloud infrastructure, data integration, and production-grade distributed systems.

Key Responsibilities
Lead the architecture and development of the EBC backend/runtime platform, including APIs, session management, identity context, and integrations.
Design and develop scalable backend services using advanced Python, asynchronous processing, REST APIs, and streaming APIs.
Implement robust workflow and session-state management, including retries, timeouts, caching, schema validation, and structured error handling.
Establish API-first architecture using Apigee, versioned contracts, authentication, authorization, rate limiting, and user-context propagation.
Integrate backend services with UI applications, AI gateways, agents, BigQuery, Stardog, and other enterprise platforms.
Design efficient data-access patterns using advanced SQL and BigQuery, including curated/authorized views, query optimization, filtering, pagination, transformation, masking, and role-aware data delivery.
Integrate conversational AI and agent capabilities while supporting structured AI responses, citations, and graceful handling of unavailable downstream services.
Integrate backend applications with semantic and knowledge-graph interfaces.
Design and support cloud infrastructure using Google Cloud Platform, GKE, Docker, Kubernetes, and Terraform.
Implement service accounts, secrets management, workload identity, networking, DNS, load balancing, and private connectivity.
Establish automated CI/CD pipelines covering build, testing, deployment, promotion, rollback, credential rotation, and environment management.
Drive unit, integration, contract, regression, performance, load, and resilience testing.
Implement production observability through logging, metrics, distributed tracing, dashboards, alerting, and SLIs.
Lead incident response, root-cause analysis, recovery procedures, and production support practices.
Ensure secure coding, least-privilege access, identity propagation, secrets management, masking, audit logging, and protection of sensitive information.
Conduct architecture, design, and code reviews while mentoring engineers and establishing reusable engineering standards.

Required Qualifications
Strong hands-on experience with Python and production backend systems.
Expertise in REST and streaming APIs and asynchronous application architectures.
Experience with Apigee or comparable API-management platforms.
Strong knowledge of authentication, authorization, identity propagation, and API security.
Advanced SQL and BigQuery experience.
Experience integrating applications with AI/agent platforms and enterprise data systems.
Strong experience with Google Cloud Platform, GKE, Kubernetes, Docker, and Terraform.
Experience designing CI/CD pipelines and production deployment processes.
Strong understanding of distributed-system reliability, observability, testing, and security.
Demonstrated experience leading technical design, conducting code reviews, and mentoring engineers.