ashby
Staff Level AI Engineer
Vendelux
Remote, USA
Full-time
Senior · 8+ years experience
Remote
Discovered 1 weeks ago
PythonSQLHuggingFaceLangChainAirflowDagster
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Key Responsibilities
- Applied AI Innovation: 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.
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