Lead Software Engineer

Vertex Inc.

12d

About The Position

Lead Software Engineer (Performance Engineering + AI Automation) Summary Vertex is seeking a Lead Software Engineer to drive performance engineering, AI‑driven automation, and technical leadership across our cloud‑native SaaS platform. This role is highly hands‑on and highly influential—owning the design and delivery of scalable performance solutions, intelligent automation, and AI-powered workflows that materially improve engineering outcomes. You will serve as a technical authority and force multiplier, embedding with engineering teams to ensure performance, reliability, and scalability are built in from day one. Must‑Haves / Non‑Negotiables Candidates must demonstrate strong, real‑world experience in the areas below. These are critical to success in this role. Leadership & Technical Influence Across the Organization Proven ability to lead architecture and engineering decisions Experience mentoring engineers and setting technical standards at scale Advanced Software Engineering & Automation Hands‑on development of production‑grade systems and automation Strong foundation in modern, object‑oriented languages (C#, Java, Go, or similar) GitHub & Repo Hygiene (Including GitHub Actions) Expert use of GitHub for collaboration, branching strategies, reviews, and automation Version‑controlled test and automation infrastructure Performance Test Infrastructure via Version Control Designing and maintaining reusable, scalable performance test frameworks CI‑integrated performance validation and regression detection Observability, Metrics & Telemetry Intelligence Instrumentation of systems and tests using metrics, logs, and traces Data‑driven diagnosis of latency, throughput, and bottlenecks General AI Literacy Practical understanding of prompt design and structured prompting Ability to work effectively with LLM APIs and AI tooling Distributed Workflow Orchestration Designing and operating distributed workflows (AI agents + tool calling) Experience orchestrating complex automation pipelines AI‑Driven Code & Test Scenario Generation Using AI to generate test scenarios, code artifacts, and manifests Focus on accelerating delivery while maintaining quality and governance Cloud Architecture (AWS, Azure, or GCP) Designing and operating cloud‑native SaaS systems Familiarity with Infrastructure as Code (Terraform, CloudFormation, Helm) CI/CD & DevOps‑Integrated Automation Deep experience integrating automation into CI/CD pipelines GitHub Actions, Azure DevOps, Jenkins, or equivalent AI Governance & Evaluation Applying guardrails, evaluation metrics, and quality controls to AI systems Ensuring reliability, safety, and repeatability of AI outputs Performance Modeling Expertise Practical application of Little’s Law, USL, Amdahl’s Law Queueing theory experience strongly preferred

Requirements

• Leadership & Technical Influence Across the Organization Proven ability to lead architecture and engineering decisions
• Experience mentoring engineers and setting technical standards at scale
• Advanced Software Engineering & Automation Hands‑on development of production‑grade systems and automation
• Strong foundation in modern, object‑oriented languages (C#, Java, Go, or similar)
• GitHub & Repo Hygiene (Including GitHub Actions) Expert use of GitHub for collaboration, branching strategies, reviews, and automation
• Version‑controlled test and automation infrastructure
• Performance Test Infrastructure via Version Control Designing and maintaining reusable, scalable performance test frameworks
• CI‑integrated performance validation and regression detection
• Observability, Metrics & Telemetry Intelligence Instrumentation of systems and tests using metrics, logs, and traces
• Data‑driven diagnosis of latency, throughput, and bottlenecks
• General AI Literacy Practical understanding of prompt design and structured prompting
• Ability to work effectively with LLM APIs and AI tooling
• Distributed Workflow Orchestration Designing and operating distributed workflows (AI agents + tool calling)
• Experience orchestrating complex automation pipelines
• AI‑Driven Code & Test Scenario Generation Using AI to generate test scenarios, code artifacts, and manifests
• Focus on accelerating delivery while maintaining quality and governance
• Cloud Architecture (AWS, Azure, or GCP) Designing and operating cloud‑native SaaS systems
• Familiarity with Infrastructure as Code (Terraform, CloudFormation, Helm)
• CI/CD & DevOps‑Integrated Automation Deep experience integrating automation into CI/CD pipelines
• GitHub Actions, Azure DevOps, Jenkins, or equivalent
• AI Governance & Evaluation Applying guardrails, evaluation metrics, and quality controls to AI systems
• Ensuring reliability, safety, and repeatability of AI outputs
• Performance Modeling Expertise Practical application of Little’s Law, USL, Amdahl’s Law
• Queueing theory experience strongly preferred
• Bachelor’s degree in Computer Science, Engineering, or equivalent experience (Master’s preferred)
• 8–10 years experience in software and performance engineering
• 3+ years building cloud‑native SaaS systems
• Demonstrated success leading technical initiatives and mentoring engineers

Nice To Haves

• Experience delivering AI/ML or Generative AI solutions in production strongly preferred

Responsibilities

• Lead performance‑by‑design architecture and engineering decisions
• Design, build, and own AI‑powered performance engineering and automation solutions
• Architect and deploy LLM‑based agents and distributed workflows
• Develop and maintain scalable performance test frameworks integrated into CI/CD
• Embed with product teams to translate business goals into actionable performance outcomes
• Diagnose and resolve complex performance issues across the SDLC
• Mentor engineers and elevate performance and automation maturity across teams

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 501-1,000 employees