Forward Deployed Engineer (AI)

Location: Remote in New Zealand, with at least 25% of time spent onsite with strategic customers; willingness to travel is required.

Company

Snowflake provides a data and AI platform and is developing capabilities for the agentic enterprise.

What you will do

• Architect, build, and deploy enterprise-grade AI solutions and sophisticated AI agents using Snowpark, Cortex, and native LLM capabilities.
• Own solution workstreams from prototype through production, including deployment, monitoring, optimization, safety guardrails, observability, and human-review workflows.
• Define quality metrics, evaluation frameworks, and golden datasets; run systematic evaluation loops to improve accuracy, faithfulness, and safety.
• Design and ship robust, scalable, and performant code and pipelines using Python and SQL.
• Partner directly with customer data science and engineering teams as a technical resource and advisor.
• Collaborate with Product and Engineering teams, sharing customer feedback to influence Snowflake’s AI platform.

Requirements

• Bachelor’s degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
• 3+ years of professional software engineering experience.
• Experience building applications using LLMs, especially RAG and agentic workflows.
• Hands-on experience defining quality metrics, running evaluations for LLM or agent systems, and using evaluations to improve quality.
• Excellent problem-solving and communication skills, including the ability to explain complex technical concepts to diverse stakeholders.
• Comfort with ambiguity and willingness to work in a fast-paced, changing Generative AI environment.
• Willingness to travel.

Nice to have

• Experience building evaluation sets from production traces and synthetic data, and running A/B tests, ablations, and offline evaluations.
• Familiarity with Braintrust, LangSmith, Arize, Weave, Promptfoo, or custom evaluation harnesses.
• Experience analyzing failure modes in agent or RAG systems, including hallucinations, retrieval misses, planning failures, and tool misuse.
• Hands-on MLOps experience covering model deployment, monitoring, and evaluation in AWS, Azure, or GCP.
• Familiarity with pandas, NumPy, and Snowpark.
• Experience in a customer-facing technical role such as solutions architect, sales engineer, or professional services.
• Startup experience.

Culture & Benefits

• AI-native, experimental approach to solving problems and improving how work gets done.
• Low-ego environment focused on challenging ordinary thinking and moving quickly.
• Opportunity to work with strategic customers on real-world enterprise AI problems.
• At least 25% onsite customer engagement and travel.