Lead AI Platform Engineer - Guardrails

Role overview

GenAI engineering

• Design, develop, and deploy solutions using LLMs, RAG architectures, prompt engineering, and agentic AI workflows.
• Build intelligent systems with LangChain, LangGraph, AWS Bedrock, and Microsoft Foundry Agent Service.
• Evaluate emerging tools and frameworks to improve solution quality and innovation.

GenAIOps and platform

• Lead solution architecture, enterprise integration, secure deployment, release management, monitoring, observability, and optimization.
• Implement GenAIOps practices that support scalability, reliability, cost efficiency, logging, evaluation, and feedback.
• Architect GenAI applications across Azure and AWS and design distributed systems for high-throughput, low-latency workloads.

Software engineering

• Use Docker, Kubernetes, Terraform, and ARM/Bicep for containerization, orchestration, and infrastructure as code.
• Develop scalable Python applications using microservices-based architectures and secure data handling practices.
• Build CI/CD pipelines supporting automated testing, deployment, and release management.

Technical leadership

• Provide architectural leadership and make critical design decisions for complex AI solutions.
• Mentor senior engineers and development teams and partner with product, business, risk, and security stakeholders.
• Translate business requirements into scalable, secure, and resilient technical solutions.

Basic qualifications

• Bachelor’s degree or equivalent work experience.
• Six to eight years of relevant experience is listed in the basic qualifications.
• The experience section also lists eight or more years in software, platform engineering, or AI/ML solutions.
• At least two years of hands-on GenAI experience including LLMs, RAG architectures, and vector databases.

Required experience

• Strong knowledge of agentic AI concepts and frameworks such as LangChain and LangGraph.
• Experience with Azure and/or AWS, distributed systems, scalable architecture patterns, Python, and microservices.
• Experience with Docker, Kubernetes, and infrastructure as code tools.
• Demonstrated technical leadership and mentoring experience.

Preferred experience

• Enterprise or regulated-environment GenAI implementation experience.
• Familiarity with observability, AI lifecycle tooling, AI governance, security, and compliance requirements.
• Background in financial services or other highly regulated industries.

Workplace

• The role requires working from a U.S. Bank location three or more days per week.

Compensation

• The listed pay range is $139,230 to $163,800 yearly.