Software Development Engineer in Test (SDET)

Jecona is a leading software company specializing in voice AI for customer service. They are seeking a Software Development Engineer in Test (SDET) to enhance quality engineering practices by implementing automation, improving testing strategies, and collaborating with cross-functional teams to ensure high-quality software delivery.

Responsibilities:

• Act as a senior technical authority for quality engineering; shaping how quality is built into the platform through hands-on engineering, championing a high bar across teams, and driving the practices and execution that make it real
• Raise the technical bar across our team of AI-enabled QA Engineers through hands-on example, code and test review, pairing, and mentorship; leveling up how a small team builds a scalable quality engineering practice
• Help drive the company's AI-native quality engineering strategy to scale coverage, accelerate validation, and surface failures earlier
• Build AI-driven testing workflows that let a small team deliver broad, scalable validation across the platform
• Own the quality feedback loop from internal dogfooding, customer A/B testing, customer feedback triage, and bug reporting, turning real-world usage into faster fixes, better test coverage, and stronger product decisions
• Partner with Product and Engineering early to improve testability, shape acceptance criteria, and build quality in from the start
• Build and improve automation frameworks, validation tooling, and CI/CD quality gates for reliable, repeatable releases
• Use AI and analytics to expand edge-case coverage, summarize failures, and generate insights that inform roadmap and release decisions

Requirements:

• 5+ years in SDET or quality engineering roles, with significant hands-on coding, including ownership of test strategy and release quality in complex software systems
• Strong programming skills in at least one modern language (TypeScript, JavaScript, or Python), with the ability to build and own production-quality tooling and frameworks
• An AI-native approach to quality engineering. You know how to leverage AI to move faster, increase coverage, reduce manual effort, and build smarter testing and validation workflows
• Curiosity about AI systems, conversational experiences, and the nuances of testing voice-driven product behavior
• Experience building quality initiatives across teams and influencing engineers, product managers, and stakeholders around a shared quality bar
• Deep knowledge of modern testing practices, including automation, API and integration testing, regression strategy, and CI/CD-based quality workflows
• Demonstrated mastery of test design, risk-based coverage, and what should be automated versus explored manually
• Comfort using data, metrics, and analytics to evaluate quality and communicate trends, risk, and opportunities for improvement
• Excellent collaboration and communication skills, with the ability to work effectively across Engineering, Product, and customer-facing teams
• Experience testing AI and LLM-powered systems, including evaluating non-deterministic behavior, hallucinations, prompt or workflow regressions, and other quality issues unique to generative products
• Experience working in an FDE, delivery, or professional services environment, with a strong understanding of customer-facing implementation workflows and real-world quality challenges