Software Engineer - Fde

Snowflake Snowflake · Data AI · Warsaw-Lixa C, Poland · Engineering

Software Engineer - FDE (Forward Deployed Engineer) for the Cortex AI team at Snowflake. This role focuses on architecting, building, and deploying enterprise-grade AI solutions and sophisticated AI agents for strategic customers, placing the engineer at the forefront of enterprise AI. The role involves owning the end-to-end lifecycle from prototype to production, translating business objectives into scalable solutions using Python and SQL, and ensuring operational excellence through rigorous data validation and monitoring. It requires partnering with customer teams as a technical advisor and collaborating with Snowflake's Product and Engineering teams.

What you'd actually do

  1. Architect, 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.
  2. Rapidly design, iterate, and ship high-quality code and ML pipelines. Translate ambiguous business objectives into robust, scalable, and performant solutions using Python and SQL.
  3. Own the full lifecycle of AI solution implementation, from developing prototypes to deploying, monitoring, and optimizing them in secure, large-scale production environments.
  4. Partner 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.
  5. Architect and implement rigorous data validation, maintain strict SLA observability, and manage complex system interdependencies to guarantee reliable AI performance.
  6. Work 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.

Skills

Required

  • 3+ years of professional software engineering experience
  • Experience 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)
  • Proven hands-on experience with data modeling, ETL/ELT development, and performance tuning
  • Advanced proficiency in Python, with experience scripting and automating data workflows
  • Excellent problem-solving and communication skills
  • Ability to articulate complex technical concepts to diverse stakeholders

Nice to have

  • Proven experience building and productionizing applications using LLMs, especially with technologies like RAG and agentic workflows
  • Hands-on experience with the MLOps lifecycle, including model deployment, monitoring, and evaluation in a cloud environment (AWS, Azure, or GCP)
  • Strong understanding of data warehousing principles, architecture, and best practices
  • Experience in a customer-facing role (e.g., solutions architect)
  • Startup experience

What the JD emphasized

  • enterprise-grade AI solutions
  • sophisticated AI agents
  • end-to-end lifecycle from prototype to production
  • productionize AI at scale
  • deploying, monitoring, and optimizing them in secure, large-scale production environments
  • leveraging cutting-edge research and AI
  • building, evaluating and tuning applications and pipelines that involve machine learning models
  • data modeling, ETL/ELT development, and performance tuning
  • building and productionizing applications using LLMs
  • agentic workflows
  • MLOps lifecycle, including model deployment, monitoring, and evaluation

Other signals

  • Deploying AI to solve real-world business problems at a massive scale
  • Building production-grade AI systems using Snowflake AI Platform, Cortex, and our native LLM capabilities
  • Architect, build, and deploy enterprise-grade AI solutions, including sophisticated AI agents
  • Own the end-to-end lifecycle from prototype to production
  • Productionize AI at Scale
  • Deploying, monitoring, and optimizing them in secure, large-scale production environments
  • Leveraging cutting-edge research and AI to deliver impactful solutions
  • Proven hands-on experience with data modeling, ETL/ELT development, and performance tuning
  • Building and productionizing applications using LLMs, especially with technologies like RAG and agentic workflows
  • Hands-on experience with the MLOps lifecycle, including model deployment, monitoring, and evaluation in a cloud environment