Infrastructure Optimization & AI Technical Lead

KeyBank • Brooklyn, OH

6d

About The Position

The Infrastructure Optimization & AI (IOAI) organization is transforming Enterprise Technology Services from reactive operations and manual analysis toward proactive, predictive intelligence and governed Generative AI adoption. Its Operational Digital Intelligence Platform (ODP) includes conversational intelligence products such as AskIT, Ask for Network, and AskIT for NOC.

The Technical Lead bridges enterprise architecture and hands-on software development. The role defines and implements scalable Google Cloud Platform architectures while developing and reviewing production code. As a technical authority for the core platform, the lead guides cross-functional engineering squads delivering data pipelines, deterministic policy engines, indexing and retrieval systems, and Google Enterprise AI agentic workflows.

The role emphasizes secure, reliable cloud-native development; grounded AI and GraphRAG retrieval; robust data contracts and operational pipelines; and automated delivery, governance, observability, and cost efficiency. The position is based at 4910 Tiedeman Road, Brooklyn, Ohio. Key prioritizes in-office presence while providing flexible options when roles can be performed effectively in a mobile environment.

Requirements

• At least 5 years of professional software development experience, with deep proficiency in Python, including modern async patterns, typing, and Pydantic v2.
• At least 4 years of hands-on architecture and engineering experience on Google Cloud Platform.
• GCP experience with Cloud Run, Cloud Functions, Pub/Sub, and Cloud Scheduler.
• Experience with BigQuery, including partitioning, clustering, and policy tags; Cloud Storage; and Cloud Spanner.
• Experience with Secret Manager, IAM roles and service accounts, and workload identity federation.
• Direct experience architecting applications using Google Vertex AI, including Gemini models, Agent Builder, Search, grounding pipelines, and vector embeddings, or frameworks such as LangChain or LangGraph.
• Demonstrated experience designing distributed systems, RESTful APIs, event-driven pipelines, and canonical data models.
• Proven ability to lead squads, mentor developers, define architectural standards, and build consensus across engineering, product, and platform leadership.
• Bachelor’s degree in Computer Science, Software Engineering, Mathematics, or equivalent practical enterprise experience.

Nice To Haves

• Google Cloud Certified Professional Cloud Architect or Professional Data Engineer certification.
• Familiarity with enterprise network concepts such as firewalls and CIDR routing, ServiceNow ITSM/KM, or IT Operations Analytics.
• Experience building automated LLM evaluation harnesses, prompt regression suites, or synthetic evaluation datasets.
• Experience with Spanner Graph.
• Financial services experience involving enterprise governance, model risk management, and regulatory compliance.

Responsibilities

• Design and implement modular, loosely coupled GCP cloud architectures that scale independently and meet enterprise reliability and zero-trust standards.
• Establish canonical schemas, interface contracts, and reusable patterns across ingestion, normalization, storage, and AI reasoning layers.
• Translate product roadmaps and Architecture Review Board specifications into engineering milestones, technical spikes, and interface contracts.
• Mentor squad developers and partner engineering teams, including TCS engineers, through technical guidance, code reviews, and architectural direction.
• Develop high-performance, containerized Python microservices and jobs using FastAPI and Pydantic v2 on GCP Cloud Run.
• Build deterministic validation and evaluation engines that isolate critical business logic from probabilistic LLM outputs.
• Implement strict schema validation and data contracts for structured and unstructured telemetry from systems including GitLab, Palo Alto Panorama, ServiceNow, and GCP APIs.
• Design idempotent pipelines, quarantine routing for malformed payloads, and self-healing deployment models.
• Architect conversational agent workflows using Vertex AI Agent Builder, Gemini, and agentic frameworks, including custom tool routing, memory, and complexity routing.
• Implement hybrid retrieval using Spanner Graph, vector embeddings, and Vertex AI Search for grounded, multi-hop operational reasoning.
• Ensure AI-generated responses include verifiable evidence citations linked to authoritative rule IDs, commit hashes, or documentation.
• Partner with QA and AI Assurance teams on evaluation pipelines to measure grounding and latency and enforce safety gates.
• Implement automated testing, container scanning, and zero-touch deployment pipelines for repeatable, immutable cloud releases.
• Enforce ABAC, pre-retrieval entitlement filtering, Secret Manager integration, and Model Armor safety filters.
• Integrate end-to-end tracing with Cloud Logging and Cloud Monitoring while balancing compute performance and GCP cost efficiency.
• Drive engineering principles including zero trust, cloud-first ephemeral workloads, governed documentation, reusable patterns, and reduced variation.

Benefits

• Eligibility for incentive compensation, which may include production, commission, and/or discretionary incentives.
• Benefits are referenced as available for this position; specific benefits are not listed in the posting.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior