Forward Deployed Engineer, Applied AI

Snowflake

Forward Deployed Engineer building production-grade AI agents for Snowflake’s enterprise customers. Deploying and evaluating LLM systems with Snowpark, Cortex, Python, and SQL.

Posted 8/11/2026full-timeMenlo Park • California, Washington • 🇺🇸 United StatesMid-LevelSenior💰 $126,000 - $181,700 per yearWebsite

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 building applications using LLMs and implementing evaluation frameworks. Proven ability to collaborate with cross-functional teams and articulate complex technical concepts to diverse stakeholders.

Highest-signal resume keywords

AI Solution ArchitectureLLM Application DevelopmentQuality Metrics DefinitionSystematic Evaluation ImprovementMLOps Lifecycle Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLLLMsEvaluation FrameworksQuality MetricsA/B TestingData AnalysisAgentic WorkflowsFailure-Mode AnalysisProduction Traces

Soft Skills

Problem-SolvingCommunicationStakeholder EngagementAdaptabilityCollaboration

Tools & Technologies

AWSAzureGCPPandasNumPySnowparkBraintrustLangSmithArizePromptfoo

Industry Keywords

Generative AIMLOpsData SecurityEnterprise AICustomer-Facing Experience

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 end-to-end from prototype through production
Translate customer goals into quality metrics, evaluation frameworks, and golden datasets
Run systematic evaluation loops to improve agent accuracy, faithfulness, and safety
Design, iterate, and ship robust, scalable, performant 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 evaluations
Partner directly with customer data science and engineering teams as a technical advisor
Collaborate cross-functionally with 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 RAG and agentic workflows
Hands-on experience defining quality metrics and running evaluations for LLM or agent systems
Experience using evaluations to systematically improve quality
Excellent problem-solving and communication skills
Ability to articulate complex technical concepts to diverse stakeholders
Comfort with ambiguity and fast-paced, changing Generative AI environments
Preferred: experience building evaluation sets from production traces and synthetic data
Preferred: structured experimentation, including A/B tests, ablations, and offline evaluations
Preferred: familiarity with Braintrust, LangSmith, Arize, Weave, Promptfoo, or custom evaluation harnesses
Preferred: failure-mode analysis for agent or RAG systems
Preferred: MLOps lifecycle experience in AWS, Azure, or GCP
Preferred: familiarity with pandas, NumPy, or Snowpark
Preferred: customer-facing technical experience
Preferred: startup experience
Must follow Snowflake confidentiality and security standards and data security plan

Benefits

Comp & perks

Salary range of $126K–$181.7K per year
Opportunity to travel and work onsite with strategic customers
Professional impact through collaboration with Product and Engineering teams
Snowflake employee confidentiality and security standards