Staff AI Engineer

Key Responsibilities

• Research and apply modern methods (AI agents, LLMs, RAG, embeddings, supervised/unsupervised learning) to power new product capabilities, prioritizing impact and speed over perfection.
• Enhance User Experience Through AI: Translate business problems into AI solutions that improve user workflows, accelerate insights, and drive measurable value.
• Build Agentic Chat Across the Product: Own the architecture and integration of agentic chat capabilities throughout the Vendelux platform — designing the underlying systems that allow AI agents to reason, take action, and surface insights across every surface of the product.
• You'll establish the patterns for how agents interact with internal data, external tools, and user workflows, turning conversational AI from a feature into a foundational layer of the product experience.
• Build for Scale and Reliability: Architect multi-tenant AI infrastructure that meets the performance, latency, and cost requirements of a growing B2B SaaS product. You'll own observability, evaluation frameworks, and guardrails to keep AI features production-grade.
• Partner Across Product and Engineering: Work closely with product, data science, and application engineering teams to translate business requirements into AI systems. You'll serve as a thought partner to leadership on the AI roadmap, influencing how Vendelux thinks about product differentiation through AI.

Qualifications

• 8+ years of professional experience in software engineering or data engineering, with at least 3+ years building AI/ML-driven products.
• Strong programming skills in Python and proficiency in SQL.
• Experience with modern AI frameworks (eg HuggingFace, LangChain, etc.) and designing data pipelines (Airflow, Dagster, or equivalent).
• Solid understanding of cloud-based infrastructure (AWS, GCP, or Azure).
• Experience applying LLMs and generative AI in production, including prompt engineering, RAG, and LLM deployment.
• Strong grasp of fundamental data science and machine learning techniques, with the ability to balance research and engineering tradeoffs.
• Demonstrated ability to translate business problems into data solutions that drive business value.
• Proven track record of shipping user-facing features with measurable impact.
• Excellent written and verbal communication skills; ability to collaborate across data, engineering, and product teams.