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.