Senior GenAI Engineer

A platform that automates billing, revenue recognition, and SaaS metrics in one unified system. Having successfully built the product, the company is now scaling its core team and looking for curious, driven individuals excited to co-build its next phase.

About the Role

As a Senior GenAI Engineer, you will own the AI layer of our product, building intelligent features for enterprise clients. This is a production-focused role, emphasizing reliability, observability, and rigorous evaluations for AI systems. You will own the full vertical, from model to pipeline and UI, delivering high-impact features like CS Copilot and document understanding.

Key Responsibilities

• Build and own CS Copilot, including STT pipelines, live transcription, and LLM-powered suggestions.
• Build LLM-powered document understanding features for extracting structured data from unstructured enterprise documents.
• Own AI feature UIs end-to-end, building the interface in addition to model integration.
• Design and maintain an eval framework to define AI feature quality and catch regressions.
• Drive model selection and integration decisions, managing latency and cost.
• Own AI platform reliability, including observability, fallback behavior, and graceful degradation.
• Work closely with product, customer success, and full-stack engineers to ensure usability and trust.

Qualifications

• Minimum of 4 years of software development experience, with at least 2 years focused on building and shipping production-grade AI/LLM features.
• Bachelor's or Master's degree in Computer Science or a related technical field, or equivalent practical experience, with strong engineering fundamentals.
• Demonstrated ability to articulate complex AI problems solved, highlighting key learnings and iterative improvements.
• Proven expertise in LLM pipeline engineering, including chaining, tool use, structured outputs, and robust prompt management.
• Experience with prompt lifecycle management, including versioning and testing.
• Strong understanding and practical experience with RAG fundamentals (chunking, retrieval, embedding models).
• Demonstrated discipline in designing and implementing AI evaluation frameworks and metrics.
• Proven ability to optimize AI model latency and cost for production environments.
• Strong awareness and practical application of Responsible AI principles and data privacy, particularly with enterprise data.
• Experience building product-quality frontend UIs for AI features (e.g., React).
• Robust backend engineering skills for end-to-end feature ownership.
• Experience designing and implementing Agentic systems, including multi-step agents and tool orchestration.
• Familiarity with Voice and multimodal AI pipelines (STT, real-time audio processing).
• Practical experience with fine-tuning open-source models (e.g., LoRA/QLoRA).
• Experience with open-source model deployment platforms (e.g., vLLM, Ollama).
• Prior experience in B2B SaaS or enterprise AI environments.
• Domain experience with financial systems, billing platforms, or fintech applications.
• Understanding of SaaS business models and relevant compliance frameworks.
• Strong API design and integration experience.
• Experience working in a fast-paced startup environment.