Applied AI Engineer
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.
About the job
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
- Running structured experimentation (A/B tests, ablations, offline evals)
- Familiarity with eval and observability tooling (e.g., Braintrust, LangSmith, Arize, Weave, Promptfoo) or 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 cloud environments (AWS, Azure, or GCP)
- Familiarity with core data science libraries and tools (e.g., pandas, numpy, Snowpark)
- Experience in a customer-facing technical role (e.g., solutions architect, sales engineer, or professional services)
- Startup experience
Skills
Python, SQL, LLMs, RAG, AI Agents, MLOps, Snowpark, AWS, Azure, GCP
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