Software Development Engineer in Test (SDET)

Jecona is a software company that is the industry leader in voice AI for customer service. As a Software Development Engineer in Test (SDET), you will work closely with Engineering, Product, and Delivery to enhance quality practices, develop automation frameworks, and leverage AI to improve testing and validation workflows.

Responsibilities:

• Act as a senior technical authority for quality engineering at Replicant; 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 Replicant'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