Who is Litmus Litmus is building the data foundation that powers industrial AI. AI doesn’t work without real-world, contextualized data - Litmus makes that data usable. As AI adoption accelerates, most industrial environments still can’t access or use their operational data. We solve that gap. We’re a growth-stage software company helping manufacturers access, structure, and use real-time data from machines, systems, and sensors at the edge. Our platform sits at the intersection of edge computing, AI, and industrial operations, enabling some of the world’s largest companies to run operations in real time, reduce downtime, and optimize production. Backed by leading investors and trusted by global manufacturers and partners like Google, Microsoft, Dell, Oracle, and Mitsubishi, Litmus is powering the shift toward software‑defined manufacturing. Why join Litmus Build the infrastructure that makes industrial AI possible AI is moving beyond the cloud and into the physical world. At Litmus, you’ll build the infrastructure that enables real‑time data to power AI and machine learning systems in production environments. Work on problems where software meets the real world Most AI systems fail without access to real‑world data. You’ll build the layer that makes them viable in production. We solve challenges at the intersection of distributed systems, real‑time data, and industrial constraints — where reliability, scale, and performance are non‑negotiable. Have real impact, fast You’ll work on systems used by real customers in production, with direct impact on product and company trajectory. As a scaling company, we move quickly. You’ll have ownership, visibility, and the ability to shape both product and company as we scale. Join a high-performance team We’re building a team that holds a high bar and pushes each other to improve. You’ll work alongside experienced operators, engineers, and leaders who have done this before and are building again at scale. We hire people who take ownership, move quickly, and care about outcomes. No passengers. Our culture At Litmus, the team is collaborative, curious, and low ego. People are scrappy, take ownership, and look for ways to make an impact. We value empathy just as much as execution, whether that’s in how we build, how we communicate, or how we support each other. We’re a growing company, so things move quickly and not everything is perfectly defined. If you enjoy figuring things out, working closely with others, and making steady progress, you’ll do well here. About the Role We are looking for a senior engineering leader to build and lead our global QA and test automation organization. This person will own quality strategy across our entire product portfolio, with a mandate to shift us from manual‑heavy testing toward a modern, automation‑first, AI‑augmented approach. A critical part of this role is establishing and scaling a high‑performing QA center of excellence in India, giving us the capacity and cost structure to support sustained growth. This leader must bring deep, hands‑on experience with packaged/on‑premise software — not SaaS — and understand the unique quality challenges that come with industrial IoT deployments, including embedded systems, edge computing, firmware, and connectivity in harsh field environments. Equally important, this person must have a forward‑looking vision for how AI and large language models can fundamentally transform QA productivity, test generation, defect prediction, and overall engineering velocity. Key Responsibilities Define and execute a company‑wide quality and test automation strategy aligned to our product roadmap and release cadence for packaged software. Build, scale, and manage a global QA organization spanning North America, Europe, and India, with a near‑term priority on standing up a cost‑effective QA hub in India. Drive adoption of modern test automation frameworks — specifically Cypress and Playwright — across web, API, and integration testing layers. Champion Behavior‑Driven Development (BDD) practices using tools like Cucumber or SpecFlow, embedding quality into the development lifecycle from requirements through delivery. Lead the AI enablement strategy for QA, including adopting AI‑powered tools for automated test case generation, self‑healing test scripts, intelligent test prioritization, and predictive defect analytics. Evaluate and integrate AI coding assistants (e.g., GitHub Copilot, Claude, ChatGPT) into QA workflows to accelerate test authoring, code review, and documentation. Build frameworks for using LLMs to convert BDD scenarios, user stories, and product requirements into executable test suites with minimal manual effort. Establish automation pipelines integrated into CI/CD for packaged software builds, including regression, smoke, and release qualification suites. Own quality strategy for industrial IoT products, addressing hardware‑software integration testing, protocol validation (MQTT, OPC‑UA, Modbus, etc.), and edge device reliability. Partner with R&D, Product, and DevOps leadership to embed quality gates and shift‑left testing practices across all teams. Define KPIs and reporting for test coverage, defect escape rates, automation ROI, AI‑driven productivity gains, and team productivity, and present regularly to the executive team. Recruit, mentor, and retain top QA talent globally, building a culture of engineering excellence, continuous improvement, and AI fluency across the team. Manage vendor relationships and outsourcing partnerships as needed to supplement the India team during ramp‑up. Required Experience & Qualifications 12+ years in software quality engineering, with at least 5 years in a senior leadership role managing globally distributed teams. Proven track record of building QA teams in India from the ground up, including site selection, hiring, onboarding, and operational maturity. Strong hands‑on background with Cypress and Playwright for end‑to‑end and component test automation. Deep experience implementing BDD frameworks and fostering collaboration between QA, product, and engineering on living documentation. Demonstrated experience applying AI and machine learning to QA processes — including AI‑assisted test generation, intelligent test selection, visual regression AI, or predictive quality analytics. Extensive experience with packaged/installed software (not SaaS/cloud‑native), including installer testing, upgrade/migration testing, on‑premise deployment validation, and customer environment variability. Domain knowledge in industrial IoT, manufacturing, or operational technology — understanding of protocols, sensor integration, edge computing, and field reliability requirements. Fluency in CI/CD toolchains (GitHub Actions, GitLab CI, Azure DevOps) and test infrastructure (Docker, virtualized labs, device farms). Experience managing budgets and building business cases for offshore/nearshore investment. Preferred Qualifications Hands‑on experience with AI testing platforms (e.g., Applitools, Testim, Mabl, or custom LLM‑based testing pipelines). Experience building internal tools or workflows that leverage LLM APIs to automate repetitive QA tasks. Experience with performance and load testing tools (JMeter, k6, Gatling). Familiarity with security testing practices for IoT and industrial environments. Background in regulated industries (energy, utilities, manufacturing) where compliance and audit trails matter. Experience with ISTQB or similar quality frameworks at an organizational level #J-18808-Ljbffr