Staff Forward Deployed Engineer, Applied AI
About The Position
As a Staff Forward Deployed Engineer, Applied AI on Snowflake’s Cortex AI team, you will be a hands-on technical leader and trusted partner to strategic customers. You will own end-to-end delivery of enterprise AI programs, lead and mentor a team of engineers, set technical direction for customer engagements, and represent a senior technical voice across product, engineering, and customer success. This is a customer-facing position requiring frequent travel within the United States, with at least 25% of time spent onsite with customers. You will lead complex AI engagements from scoping and architecture through deployment, monitoring, and handoff, and be accountable for delivery quality and customer outcomes. You will define measurable quality metrics, evaluation frameworks, and datasets, then use systematic evaluation loops to improve agent quality and maintain accuracy, faithfulness, and safety. You will remain a hands-on contributor, designing and shipping ML pipelines and agentic AI solutions, and own production implementation, guardrails, observability, human-review workflows, and ongoing optimization. You will advise customer data science and engineering leaders, collaborate with Snowflake Product and Engineering, and turn recurring deployment patterns into reusable assets and product feedback. Employees are expected to follow Snowflake’s confidentiality and security standards and protect customer information.
Requirements
- Demonstrated experience leading technical projects or teams, including setting technical direction, reviewing work, and driving delivery to completion.
- Experience building and productionizing applications using LLMs, particularly RAG and agentic workflows.
- Hands-on experience defining quality metrics and evaluation frameworks for LLM or agent systems and using evaluations to improve quality over time.
- Strong problem-solving and communication skills, including the ability to explain complex technical concepts to technical and executive stakeholders.
- Ability to work with ambiguity and independently structure and execute complex, open-ended problems.
- At least 7 years of professional software engineering experience.
- Experience in a customer-facing technical role.
- Willingness to travel.
- Ability to comply with confidentiality and data security standards and safeguard customer information.
Nice To Haves
- Experience building evaluation sets from production traces and synthetic data, and conducting structured experimentation such as A/B tests, ablations, and offline evaluations.
- Familiarity with evaluation and observability tools such as Braintrust, LangSmith, Arize, Weave, or Promptfoo, or experience building custom evaluation harnesses.
- Experience analyzing failure modes in agent or RAG systems and using targeted evaluations to address issues such as hallucinations, retrieval misses, planning failures, and tool misuse.
- Hands-on experience with the MLOps lifecycle, including model deployment, monitoring, and evaluation in AWS, Azure, or GCP.
- Familiarity with data science libraries and tools such as pandas, numpy, and Snowpark.
- Startup experience or experience in a high-growth, fast-paced environment.
Responsibilities
- Lead complex, multi-engineer AI customer programs from scoping and architecture through deployment, monitoring, and handoff; own delivery quality and customer outcomes.
- Define customer-specific AI quality metrics, evaluation frameworks, and golden datasets, and run systematic evaluation loops to improve quality and catch regressions.
- Provide technical leadership and mentorship to Applied AI Engineers, review designs and code, unblock teammates, and support their career development.
- Design, iterate on, and ship ML pipelines and agentic AI solutions while translating business objectives into robust, scalable, performant solutions.
- Own the AI implementation lifecycle from prototype through production deployment, monitoring, and optimization in secure, large-scale environments.
- Build safety guardrails, observability, and human-review workflows; use production traces and user feedback to improve evaluations and system quality.
- Advise customer data science and engineering leadership and communicate technical concepts to technical and executive stakeholders.
- Collaborate with Snowflake Product and Engineering teams and share field feedback to shape the AI platform.
- Identify recurring deployment patterns and develop reusable assets such as reference architectures and evaluation harnesses.
- Travel to work onsite with strategic customers for at least 25% of work time.
- Follow company confidentiality and security standards and protect customer information.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
