Snowflake1 день назад
Software Engineer - FDE
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Полная занятостьОфис
Обязанности
- 01Architect, build, and deploy enterprise-grade AI solutions, including sophisticated AI agents. Own the end-to-end lifecycle from prototype to production, directly solving our customers' most complex business challenges.
- 02Rapidly design, iterate, and ship high-quality code and ML pipelines. Translate ambiguous business objectives into robust, scalable, and performant solutions using Python and SQL.
- 03Own the full lifecycle of AI solution implementation, from developing prototypes to deploying, monitoring, and optimizing them in secure, large-scale production environments.
- 04Partner directly with customer data science and engineering teams, serving as a technical expert and trusted advisor on how to best leverage AI for their business challenges.
- 05Architect and implement rigorous data validation, maintain strict SLA observability, and manage complex system interdependencies to guarantee reliable AI performance.
- 06Work cross-functionally with Snowflake’s Product and Engineering teams to share real-world feedback from the customers, directly influencing the future of Snowflake's AI platform.
Требования
- 01Bachelor’s degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
- 023+ years of professional software engineering experience
- 03A passion for tackling complex and ambiguous technical challenges, leveraging cutting-edge research and AI to deliver impactful solutions.
- 04Experience building, evaluating and tuning applications and pipelines that involve machine learning models or data-intensive systems. Familiarity with core data science libraries and tools (e.g., pandas, numpy, Snowpark).
- 05Proven hands-on experience with data modeling, ETL/ELT development, and performance tuning.
- 06Advanced proficiency in Python, with experience scripting and automating data workflows.
- 07Excellent problem-solving and communication skills, with an ability to articulate complex technical concepts to diverse stakeholders.
- 08A desire to thrive in a fast-paced, dynamic environment and the ability to adapt quickly to the ever-changing world of Generative AI.
- 09Proven experience building and productionizing applications using LLMs, especially with technologies like RAG and agentic workflows.
- 10Hands-on experience with the MLOps lifecycle, including model deployment, monitoring, and evaluation in a cloud environment (AWS, Azure, or GCP).
- 11Strong understanding of data warehousing principles, architecture, and best practices.
- 12Experience in a customer-facing role (e.g., solutions architect).
- 13Startup experience