Staff AI Engineer

About the Role

We are looking for a Staff AI Engineer who thrives at the intersection of rapid experimentation and agile product development. In this role, you will be the bridge between the latest AI developments and tangible product impact. You aren't just following a roadmap; you are helping define it by proving what is possible with the latest frontier models and architectures. You will be responsible for the "quality loop": moving from a promising proof-of-concept to a highly reliable, optimized, and validated product.

What You’ll Be Doing

• Product-Centric Development: Designing and executing experiments to improve GenAI capabilities. This isn't just about "accuracy" in a vacuum—it's about optimizing for user value, reliability, and cost-effectiveness.
• Evaluation Systems: Building the "Golden Path" for quality. You will design and implement robust, multi-dimensional evaluation suites (e.g., using "LLM-as-a-judge," semantic checks, and unit tests) to ensure our features are production-ready and hallucination-resistant.
• Advanced RAG & Reasoning Optimization: Moving beyond "naive RAG." You will implement and tune advanced retrieval strategies (e.g., hybrid search, reranking, agentic retrieval) and optimize complex reasoning loops (e.g., CoT, ReAct) to make our current and future agents smarter and more reliable.
• Production-Grade Model Tuning: Leading the strategy for when, and if, to move beyond simple prompting. You will oversee supervised fine-tuning (SFT) and Parameter-Efficient Fine-Tuning (LoRA) workflows to adapt models to our specific product domains.
• Performance & Cost Engineering: Balancing the "Quality-Cost-Latency" triangle. You will find ways to maintain high-quality outputs while optimizing token usage and reducing inference latency.

What You’ll Need

• Applied MLE Background: You have a proven track record of shipping Machine Learning functionality in a product-focused environment. You prefer "what works in practice" over "what works in theory."
• Bleeding-Edge Awareness: You are a "first adopter" of new AI technologies. You are intimately familiar with the trade-offs between frontier models and know how to swap or hybridize them for maximum impact.
• Experimental & Analytical Mindset: You understand the importance of controlled experiments. You know how to design a benchmark, create a "Gold Dataset," and use data to prove that a new prompt or model is an actual improvement.
• Practical LLM Expertise: Deep, hands-on experience with LLM orchestration, vector databases, and evaluation frameworks.
• Technical Stack Mastery: Expert-level Python and experience writing production-grade code.
• Product Mindset: You think about the user. You can identify when a model’s behavior might be technically correct but results in a poor user experience, and you know how to iterate to improve it.
• Experience: 5+ years of experience in ML or Software Engineering roles, with at least 2+ years of hands-on experience building and scaling GenAI/LLM-powered features.
• Self-starter, able to take ownership and initiative, with high energy and stamina
• Decisive and action-oriented, able to make rapid decisions even when they are short of information
• Highly motivated, independent and deeply passionate about sustainability and impact
• Excellent oral and written English communication skills (minimum C1 level-proficient user)

Nice To Have

• Experience in a start-up
• Cloud AI Ecosystem: Familiarity with managed GenAI platforms and services such as OpenAI, Anthropic, AWS Bedrock, or GCP Vertex AI.
• Active Builder Credentials: A portfolio or GitHub showcasing side projects that experiment with the latest AI paradigms (e.g., multi-agent systems, local LLM execution, etc.).