Lead Software Engineer

Vertex Inc.

19h

About The Position

Lead Software Engineer Job Summary Are you a software engineering technical leader looking to drive the performance and scale for market leading SAAS products? Join us as we turn vision into reality! The ideal candidate is passionate about providing software engineering solutions applied to performance engineering delivery where business outcomes are our playground. About Us At Vertex, we build the technology that keeps global commerce moving. Every transaction, every return, every compliance calculation—our systems are trusted by thousands of the world’s most complex businesses. As we scale our SaaS platform and products, we’re engineering for extreme performance, precision, and resilience across diverse customer workloads. We are investing deeply in AI‑driven engineering, using intelligent automation, advanced LLMs, and performant architectures to transform how our products are built and operated. Engineers here don’t just write features—they solve high‑leverage systems problems, model and optimize performance at scale, and design automation that becomes force multipliers for entire value streams. Our teams are collaborative, curious, and serious about their craft. We value people who take ownership, push boundaries, and elevate those around them. We foster an environment where engineers can stretch, lead, and continuously deepen their expertise—whether in distributed systems, performance engineering, or cutting‑edge AI. If you’re driven to build systems that truly scale, to solve meaningful engineering challenges, and to grow your career while shaping the future of intelligent performance automation, you’ll thrive here.

Requirements

Education: Master’s degree (preferred) or bachelor’s degree in computer science, engineering, or a related field (or equivalent experience).
Experience: 8 - 10 years in a combination of software and performance engineering, with at least 3 years in Cloud Native SaaS applications and preferably 2-3 years mentoring and coaching engineers in a leadership role.
Technical Skills Experience in object-oriented/modern languages (C#, Java, Go etc.)
Proven ability to lead technical engagements, mentor junior team members, and oversee technical aspects of projects
Proven ability to implement and drive performance test methodologies, principles, patterns, and practices across multiple parallel initiatives
Demonstrated accountability, a "get-things-done" attitude, and a focus on fast, impactful delivery, with a proven ability to lead technical initiatives
Proficient in RESTful API, Open API design and development, and database technologies (RDBMS, NoSQL).
Proficiency with CI/CD pipelines, containerization (Docker, Kubernetes) and infrastructure as code (Terraform, CloudFormation, etc.).
Experience in designing and implementing scalable test automation strategies using modern frameworks and tools such as k6, Locust, JMeter., with integration into CI/CD pipelines (e.g., GitHub Actions, Azure DevOps, Jenkins) and cloud-based testing platforms.
Experience designing and delivering AI solutions using modern software engineering practices and cloud technologies (e.g., Azure, AWS, GCP), with a strong focus on Generative AI and Large Language Models (LLMs).
Understanding of security best practices and frameworks.
Excellent communication, interpersonal, presentation, and facilitation skills across time zones.

Nice To Haves

Experience working on AI/ML initiatives or certifications in AI technologies is a strong plus.
Knowledge of queueing theory and performance models (USL, Little’s Law, Amdahl’s Law) a plus.
Knowledge of Snyk is a plus.

Responsibilities

Technical Leadership: Apply software and performance engineering practices to architecture, and technology choices that lead to high performing, scalable solutions. Lead and mentor junior engineers, guide technical design and architecture discussions with a performance by design approach.
Build Transformative AI Solutions: Drive tangible outcomes through hands-on implementation for the end-to-end technical delivery of performance engineering AI solutions by creating or leveraging data for ingestion by LLMs with NLP APIs to accelerate performance testing and delivery outcomes.
Accountability: Take primary ownership of designing, developing, and personally implementing high-quality performance engineering solutions. A significant part of this will involve architecting and deploying complex AI and Large Language Model (LLM) based intelligent agents and automated workflows, all built to deliver automation and performance engineering outcomes.
Become a Trusted Strategic Partner: Embed yourself deeply with value stream engineering teams to truly grasp their performance needs, challenges and strategic product goals. You'll translate these nuanced needs into clear, actionable technical performance goals and solution designs, building strong relationships as their primary performance advisor and leading performance discussions and optimizing features.
Proactively Remove Technical Blockers: Identify, analyze, and directly resolve complex technical performance challenges during all phases of release delivery, from planning, design, to sophisticated performance test automation and advanced agent orchestration. You'll be the ultimate go-to expert for driving value stream product performance.
Development: Design, code (hands-on), and maintain (proactive monitoring, troubleshooting) software solutions using object-oriented or modern languages (C#, Java, Go etc.)
Test: Create test scenarios and design, develop, and execute corresponding automated tests.
Cloud Infrastructure: Experience with major cloud providers like AWS, Azure or GCP. Understanding of Infrastructure as Code (e.g. Terraform, CloudFormation, Helm etc.)
Collaboration: Excel in global cross-functional teams, including product management, senior leaders, engineering and architecture.
Code Reviews: Utilize code reviews to ensure code quality, performance and maintainability, as well as mentoring opportunities.
Continuous Improvement: Champion a culture of continuous improvement by driving engineering excellence, conducting root cause analyses, and implementing feedback loops to enhance code quality, team productivity, and system reliability.