Golang Software Engineering Lead - GenAI platforms - Senior Vice President

Golang Software Engineering Lead - GenAI platforms - Senior Vice President

Job Overview

About the Role

We're seeking an exceptional Golang Software Engineering Lead - GenAI platforms to drive the backend technical vision and full-stack execution of our enterprise GenAI platform serving 180,000+ Citi employees globally. This is a senior technical leadership role for someone who wants to architect scalable, high-performance AI systems at the intersection of modern cloud-native development and cutting-edge AI—combining hands-on engineering excellence with strategic technical leadership.

You'll work with cutting-edge AI infrastructure including Claude, Gemini, and proprietary Citi models running on OpenShift/Kubernetes, building the next generation of AI-powered backend services and microservices that transform how employees interact with enterprise AI systems.

About Our Team

Our team operates like a research-driven startup within Citi, rapidly innovating on AI user experiences while maintaining enterprise-grade reliability, security, and compliance. We build and operate Citi Stylus Workspaces and other mission-critical GenAI platforms that demand exceptional scalability, performance, and reliability at global scale.

Our platforms integrate cutting-edge AI models to provide secure, compliant, and powerful AI capabilities across the organization. Our microservices architecture is built with Go and React, deployed on OpenShift/Kubernetes, and incorporates sophisticated document understanding, agentic capabilities, and integration with numerous internal systems.

What You'll Do

• Architecture & Development

• Design, develop, and maintain core components of our production GenAI platform

• Architect new systems and services that scale to enterprise requirements (180,000+ users)

• Implement complex features across the entire stack, from backend services to frontend interfaces

• Design and implement scalable microservices architecture for complex GenAI applications

• Build sophisticated document processing and transformation pipelines

• Optimize system performance, particularly for AI-related operations and high-throughput scenarios

• Collaborate with AI researchers to implement state-of-the-art techniques

• Develop real-time streaming architectures for AI responses using WebSockets and Server-Sent Events

• Implement advanced caching strategies and distributed system patterns

• AI/ML Engineering

• Build practical LLM-based applications with production-grade reliability

• Implement prompt engineering techniques and patterns for enterprise use cases

• Architect vector database solutions and semantic search capabilities

• Design streaming architectures for AI responses and real-time collaboration

• Integrate multiple LLM providers (Claude, Gemini, proprietary models)

• Develop agentic capabilities and multi-agent system orchestration

• Optimize AI inference performance and cost efficiency

• DevOps & Production

• Design and implement observability solutions for AI-specific metrics and general system health

• Create and maintain deployment pipelines and configuration for multiple environments

• Build comprehensive CI/CD pipelines using GitOps workflows

• Participate in production support rotation and incident response

• Lead production incident response, root cause analysis, and blameless postmortem processes

• Analyze and resolve complex production issues across the stack

• Implement monitoring, error tracking, and alerting for production applications

• Optimize build processes and deployment strategies for performance

• Cloud & Infrastructure

• Design and implement Kubernetes/OpenShift deployment patterns and Helm charts

• Architect service mesh implementations (Istio) for microservices communication

• Implement infrastructure-as-code and GitOps workflows

• Design network architecture for distributed systems

• Ensure security best practices including OAuth/JWT, Vault integration, and document classification

• Build container-based deployment strategies with high availability

• Source knowledge of Helm charts and Kubernetes operators

• Leadership & Collaboration

• Define technical vision and roadmap for GenAI platform backend excellence and full-stack capabilities

• Set technical vision and drive architectural direction across multiple services and teams

• Provide technical mentorship to engineering teams and develop technical talent

• Lead architectural discussions and make strategic technical decisions

• Partner with engineering, security, and business leaders to align technology strategy with organizational objectives

• Drive engineering excellence through code reviews and best practice implementation

• Represent the engineering organization in cross-functional leadership forums

• Lead cross-functional collaboration with product managers, AI researchers, and frontend engineers

• Build and lead high-performing engineering teams

• What You Bring

• Core Technical Expertise (Must-Have)

• Programming & Software Design

• Expert-level Go programming (5+ years) with deep understanding of concurrency patterns

• Proficiency with TypeScript/JavaScript and React (3+ years) for full-stack development

• Strong understanding of clean architecture, SOLID principles, and design patterns

• Experience with concurrent and parallel programming

• Comfort with both statically and dynamically typed languages

• Advanced knowledge of microservices architecture and API design

• Deep understanding of RESTful APIs, gRPC, and real-time communication protocols

• Cloud & Infrastructure

• Deep understanding of Kubernetes/OpenShift architecture and deployment patterns

• Experience with service mesh implementations (Istio preferred)

• Knowledge of infrastructure-as-code and GitOps workflows

• Understanding of network architecture for distributed systems

• Experience with containerization (Docker) and orchestration at scale

• Proficiency with Helm charts and Kubernetes operators

• AI/ML Engineering

• Practical experience implementing LLM-based applications in production environments

• Knowledge of prompt engineering techniques and patterns

• Understanding of vector databases and semantic search

• Experience with streaming architectures for AI responses

• Familiarity with AI model integration, fine-tuning, and optimization

• Understanding of RAG (Retrieval-Augmented Generation) patterns

• Data & Systems

• Experience with document processing and transformation pipelines

• Knowledge of NoSQL databases, particularly MongoDB

• Understanding of caching strategies and implementations (Redis)

• Experience with high-throughput, low-latency distributed systems

• Knowledge of S3-compatible object storage and data management

• Understanding of data consistency patterns in distributed systems

• DevOps & Reliability

• Strong understanding of observability(metrics, traces, logs)

• Experience with CI/CD pipelines and automated testing

• Knowledge of performance testing and optimization techniques

• Experience with production incident management and resolution

• Understanding of SRE principles and practices

• Experience with monitoring tools (Prometheus, Grafana, ELK stack)

• Security & Compliance

• Knowledge of OAuth/JWT authentication and authorization patterns

• Experience with secrets management ( Vault)

• Understanding of security best practices for enterprise applications

• Familiarity with compliance requirements in regulated industries

• Professional Experience

• 15+ years of overall software development experience

• 5+ years in technical leadership positions

• 5+ years working with cloud-native architectures

• 3+ years practical experience with AI/ML systems in production

• Experience leading teams building enterprise-scale systems (10,000+ users)

• Track record of successfully delivering complex technical projects at organization-wide scale

• Experience building and leading high-performing engineering teams

• History of mentoring and developing engineering talent

• Experience operating in regulated industries (finance, healthcare, government)

• Nice to Have

• Experience architecting and scaling backend systems for enterprise environments

• Knowledge of micro-frontend architecture and module federation

• Understanding of GraphQL and real-time data synchronization

• Experience with AI/ML interface patterns and prompt engineering UX

• Knowledge of event-driven architectures and message queuing systems

• Experience with performance optimization and load testing at scale

• Understanding of chaos engineering and resilience testing

• Famil