Senior Forward Deployed Engineer, Applied AI

Jobtailor; California (MO); Hybrid; USD 180,000 - 280,000; Full time

Job summary

Snowflake is seeking an experienced AI Leader to own and deliver large-scale AI engagements from scoping through deployment, monitoring, and handoff. You will mentor a team of Applied AI Engineers, design ML pipelines, and build agentic workflows with strong emphasis on safety, observability, and data security.

Expect frequent collaboration with customers and product teams. Travel to customer sites will be required, with substantial onsite time to drive strategies and ensure successful

Qualifications

• Experience leading technical projects or teams and driving delivery to completion.
• Productionizing applications using LLMs, including RAG and agentic workflows.
• Defining quality metrics and evaluation frameworks for LLM or agent systems.
• Excellent problem-solving and communication skills to explain complex concepts to technical and executive stakeholders.
• Comfort with ambiguity and ability to structure and execute open-ended problems.
• 5+ years of professional software engineering experience.
• Experience in a customer-facing technical role and willingness to travel.
• Familiarity with eval and observability tools like Braintrust, LangSmith, Arize, Weave, or Promptfoo.
• Hands-on experience with MLOps lifecycle: model deployment, monitoring, and evaluation in AWS/Azure/GCP.

Responsibilities

• Own the full lifecycle of AI engagements from scoping to deployment.
• Lead and mentor a team of Applied AI Engineers; review designs and code.
• Design, iterate, and ship ML pipelines and agentic AI solutions.
• Translate business objectives into robust, scalable AI solutions.
• Build safety guardrails, observability, and human-review workflows.

Skills

AI Project Leadership

LLM Application Development

MLOps Lifecycle Management

Quality Metrics Definition

Customer-Facing Technical Experience

Productionization

Software Engineering

Technical Project Leadership

Tools

AWS

Azure

GCP

Snowflake

Braintrust

LangSmith

Arize

Weave

Promptfoo

Pandas

Job description

• Own the full lifecycle of complex, multi-engineer AI engagements from scoping and architecture through deployment, monitoring, and handoff
• Define AI quality metrics, evaluation frameworks, golden datasets, and systematic evaluation loops
• Lead and mentor a team of 2–6 Applied AI Engineers; review designs and code and unblock teammates
• Design, iterate, and ship ML pipelines and agentic AI solutions
• Translate ambiguous business objectives into robust, scalable, and performant solutions
• Own implementation from prototype through deployment, monitoring, and optimization in secure, large-scale production environments
• Build safety guardrails, observability, and human-review workflows
• Advise customer data science and engineering leadership on Snowflake AI deployment
• Articulate complex technical concepts to technical and executive stakeholders
• Collaborate with Product and Engineering teams to shape Snowflake's AI platform
• Turn recurring deployment patterns into reusable assets, reference architectures, evaluation harnesses, and product feedback
• Spend at least 25% of time onsite with strategic customers

Requirements

• Demonstrated experience leading technical projects or teams, including setting technical direction, reviewing others' work, and driving delivery to completion
• Proven experience building and productionizing applications using LLMs, especially RAG and agentic workflows
• Hands-on experience defining quality metrics and evaluation frameworks for LLM or agent systems
• Excellent problem-solving and communication skills, with ability to explain complex technical concepts to technical and executive stakeholders
• Comfort with ambiguity and ability to independently structure and execute complex, open-ended problems
• 5+ years of professional software engineering experience
• Experience in a customer-facing technical role
• Willingness to travel
• Experience building eval sets from production traces and synthetic data and running structured experimentation
• Familiarity with eval and observability tooling such as Braintrust, LangSmith, Arize, Weave, or Promptfoo, or custom eval harnesses
• Experience with failure-mode analysis on agent or RAG systems
• Hands-on experience with MLOps lifecycle, including model deployment, monitoring, and evaluation in AWS, Azure, or GCP
• Familiarity with pandas, numpy, and Snowpark
• Startup experience or experience in a high-growth, fast-paced environment
• Must follow Snowflake confidentiality and security standards and data security plan
• Must be authorized to work in the country to which applying

Core Competencies

Demonstrates expertise in leading AI projects, including defining quality metrics, building ML pipelines, and deploying solutions in large-scale environments. Proficient in articulating complex technical concepts to diverse stakeholders and mentoring teams in a customer-facing role.

Highest-signal resume keywords

• AI Project Leadership
• LLM Application Development
• MLOps Lifecycle Management
• Quality Metrics Definition
• Customer-Facing Technical Experience

ATS Optimization Keywords

Hard Skills

• Machine Learning Pipelines
• LLM Productionization
• Quality Metrics Definition
• Failure-Mode Analysis
• Model Deployment
• Structured Experimentation
• Data Evaluation Frameworks
• Agentic Workflows
• Software Engineering
• Technical Project Leadership

Soft Skills

• Problem-Solving
• Communication
• MentoringCollaboration
• Adaptability

Industry Keywords

• AI Engagements
• MLOps
• Production Environments
• Data Security
• High-Growth Environment

Tools & Technologies

• AWS
• Azure
• GCP
• Snowflake
• Braintrust
• LangSmith
• Arize
• Weave
• Promptfoo
• Pandas