Golang Software Engineering Lead - GenAI platforms - Senior Vice President

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.

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.

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

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

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

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

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

Familiarity with multiple programming languages and paradigms

Beyond technical skills, you are:

Innovative problem solver who transforms complex technical challenges into elegant, scalable solutions

Passionate about AI-powered systems and leveraging AI to revolutionize enterprise applications

Hands-on technical leader comfortable diving deep into technical details while maintaining strategic perspective

Self-driven with ability to work in a fast-paced, research-oriented environment

Strong problem-solver with a systematic approach to complex challenges

Excellent communicator able to articulate complex technical concepts to diverse audiences

Collaborative with experience working across teams (engineering, design, product, business)

Curious about emerging technologies with commitment to staying current

Pragmatic with ability to balance ideal solutions with practical constraints and timelines

Comfortable with ambiguity and ability to make progress with incomplete information

Quality-focused with strong attention to detail and commitment to engineering excellence

Cutting-Edge Technology : Work with the latest AI/ML infrastructure, modern backend frameworks, and emerging cloud-native technologies

Enterprise Scale : Build systems serving users globally with real business impact

Innovation Culture : Research-driven environment encouraging experimentation and rapid prototyping

Technical Excellence : Collaborate with world-class engineers and AI researchers

Career Growth : SVP-level technical leadership role with visibility to senior leadership

Meaningful Work : Build backend systems powering AI transformation across a global financial institution

We embrace a hybrid work model with a mix of in-office and remote work. We value innovation, technical excellence, and operational discipline. We operate with agile methodologies adapted to our specific needs, with two-week sprints, regular releases, and continuous improvement cycles.

Bachelor's degree in Computer Science , Software Engineering, or related technical field, or equivalent practical experience

Backend : Go microservices with clean architecture patterns

Frontend : React 19+ with TypeScript, modern state management ( Zustand ), TanStack Query

Infrastructure : OpenShift/Kubernetes with Helm deployments, Istio service mesh

Data Storage : MongoDB, Redis, S3-compatible object storage

APIs : REST, gRPC , WebSockets , Server-Sent Events

AI/ML : Integration with multiple LLM providers (Claude, Gemini, proprietary models), vector search, prompt engineering

Security : OAuth/JWT, Vault, document classification

Testing : Comprehensive testing strategies (unit, integration, E2E)

Build Tools : Modern CI/CD pipelines, GitOps workflows

Observability : Metrics, traces, logs, monitoring and alerting

Performance : High-throughput, low-latency distributed systems optimization

If you're passionate about building scalable, intelligent backend systems at the intersection of AI and modern cloud-native development, and want to shape the future of enterprise AI platforms at global scale, we'd love to hear from you.

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