Forward Deployed Engineer

Snowflake

. Architect, build, and deploy enterprise-grade AI solutions, including sophisticated AI agents .

Posted 10/23/2026full-timeNew ZealandMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in architecting and deploying enterprise-grade AI solutions, with a strong focus on quality metrics, evaluation frameworks, and the MLOps lifecycle. Proven ability to collaborate with technical teams and articulate complex concepts to diverse stakeholders in a fast-paced Generative AI environment.

Highest-signal resume keywords

AI Solution ArchitectureQuality Metrics DefinitionLLM Application DevelopmentMLOps Lifecycle ManagementCustomer-Facing Technical Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLLLMsQuality MetricsA/B TestingFailure-Mode AnalysisModel DeploymentMonitoringData EvaluationSynthetic Data

Soft Skills

Problem-SolvingCommunicationAdaptability

Tools & Technologies

AWSAzureGCPBraintrustLangSmithArizeWeavePromptfooPandasNumpy

Industry Keywords

Generative AIMLOpsAgent SystemsRAG WorkflowsCustomer Data Science

Tech Stack

Tools & technologies

AWSAzureGoogle Cloud PlatformNumpyPandasPythonSQL

About the role

Key responsibilities & impact

Architect, build, and deploy enterprise-grade AI solutions, including sophisticated AI agents
Own workstreams from prototype to production, solving complex customer business challenges
Define quality metrics, evaluation frameworks, and golden datasets for AI systems
Run systematic evaluation loops to improve agent quality and detect regressions
Design, iterate, and ship code and pipelines using Python and SQL
Implement, deploy, monitor, and optimize AI solutions in secure, large-scale production environments
Build safety guardrails, observability, and human-review workflows
Use production traces and user feedback to improve evaluation processes
Partner directly with customer data science and engineering teams as a technical advisor
Collaborate with Snowflake Product and Engineering teams and share customer feedback
Spend at least 25% of time onsite with strategic customers

Requirements

What you’ll need

Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience
3+ years of professional software engineering experience
Willingness to travel
Proven experience building applications using LLMs, especially with RAG and agentic workflows
Hands-on experience defining quality metrics and running evaluations for LLM or agent systems, and using evals to systematically improve quality
Excellent problem-solving and communication skills, with ability to articulate complex technical concepts to diverse stakeholders
Comfort with ambiguity and willingness to thrive in a fast-paced, changing Generative AI environment
Experience building eval sets from production traces and synthetic data, and running structured experimentation such as A/B tests, ablations, and offline evals
Familiarity with eval and observability tooling such as Braintrust, LangSmith, Arize, Weave, and Promptfoo, or experience building custom eval harnesses
Experience with failure-mode analysis on agent or RAG systems
Hands-on experience with the MLOps lifecycle, including model deployment, monitoring, and evaluation in AWS, Azure, or GCP
Familiarity with pandas, numpy, and Snowpark
Experience in a customer-facing technical role
Startup experience

Benefits

Comp & perks

Opportunity to travel and work onsite with strategic customers
Salary and benefits information is available on the Snowflake Careers Site for jobs located in the United States