Hands-on Applied AI Engineer on the Cortex AI team building and deploying production-grade AI agents and solutions for enterprise customers using Snowpark, Cortex, and native LLM capabilities.
126k – 182k/yr
On-site3+ YOEML Engineering
About the role
Responsibilities
Architect, build, and deploy enterprise-grade AI solutions, including sophisticated AI agents
Own the end-to-end lifecycle of workstreams from prototype to production, solving customers' most complex business challenges
Define quality metrics, evaluation frameworks, and golden datasets for AI systems
Run systematic eval loops to improve agent quality, catch regressions, and raise the bar on accuracy, faithfulness, and safety
Rapidly design, iterate, and ship high-quality code and pipelines using Python and SQL
Own the full implementation lifecycle including deployment, monitoring, and optimization in secure, large-scale production environments
Build safety guardrails, observability, and human-review workflows for AI applications
Close the loop from production traces and user feedback back into evaluations
Partner directly with customer data science and engineering teams as a hands-on technical resource
Work cross-functionally with Product and Engineering teams to share real-world feedback and influence the AI platform
Spend at least 25% of time onsite with strategic customers
Requirements
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 (at least 25% onsite)
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
Excellent problem-solving and communication skills
Comfort with ambiguity and thriving in a fast-paced Generative AI environment
Nice-to-Haves
Experience building eval sets from production traces and synthetic data
Designs, builds, and operates large-scale ML infrastructure for AI/RL research, including GPU cluster orchestration, data curation pipelines, and distributed training systems for autonomous driving and robotics.
126k – 423k/yr
On-siteML Engineering
Research Scientist - Reinforcement Learning, Self-Driving
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On-siteML Engineering
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On-siteML Engineering
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