Senior QA Engineer (Manual & Automation)

About the Role

We are looking for a Senior AI Software Engineer to join our R&D group and help design and implement our next-generation AI-driven data and intelligence platform. This role is ideal for a senior engineer who thrives at the intersection of software engineering, data pipelines, and applied AI — someone who can bridge solid backend foundations with practical experience in modern AI systems. You’ll lead efforts to integrate and operationalize existing open-source and commercial AI models, evaluate their performance, and optimize their cost and accuracy in production environments. Who we are: We’re a fast-paced startup committed to delivering high-quality releases and an outstanding user experience. We thrive on innovation, collaboration, and a commitment to excellence. If you’re someone who enjoys diving deep into both manual and automated testing, and you’re eager to make an impact in a dynamic environment, we’d love to hear from you. What You’ll Do Design and build scalable data pipelines to gather, prepare, and serve data for AI workflows — including evaluation, fine-tuning, and retrieval pipelines. Integrate and operationalize LLMs and multimodal models, focusing on applying and adapting existing open-source or commercial models rather than building from scratch. Conduct error analysis, model evaluation, and cost/performance optimization to ensure production readiness and efficiency. Develop and maintain backend services (primarily in Java and Python) that power AI-driven features and automate data processing. Implement retrieval-augmented generation (RAG) and information retrieval pipelines to enhance system intelligence. Apply prompt engineering, validation, and A/B testing to improve system output quality. Collaborate with product and engineering teams to turn AI capabilities into end-user features with measurable impact. Stay current with emerging AI trends and tools, driving innovation and adoption across the company. What We’re Looking For 7+ years of hands-on software engineering experience, including system design and production-grade development. B.Sc. in Computer Science or Software Engineering (M.Sc. a plus). Strong proficiency in Java and Python. Experience in cloud environments (AWS, GCP, or Azure). Hands-on experience with Kafka, SQL, and NoSQL databases. Familiarity with machine learning fundamentals, NLP, information retrieval, and RAG architectures. Experience building data pipelines for training, evaluation, or experimentation. Proven ability to conduct prompt optimization, model evaluation, and cost-aware experimentation. Self-driven mindset and curiosity to explore new tools, frameworks, and approaches in applied AI. Nice to Have Fluency with AI-assisted development (GitHub Copilot, Cursor, JetBrains AI) and experience defining org-wide usage guidelines. Experience with multi-model routing, tool-use/agents, and safety classifiers/guardrails. Knowledge of model optimization techniques (quantization, distillation) and GPU/accelerator stacks.