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.