# Senior AI/ML Architect, Applied Field Engineering/Field CTO

**Company:** [Snowflake](https://hotfix.jobs/companies/snowflake)
**Location:** Unspecified
**Role:** Sales Engineering
**Experience:** 10+ years
**Skills:** Python, Machine Learning, Generative AI, MLOps, Feature Stores, Model Deployment, RAG, Prompt Engineering, LangChain, pandas, scikit-learn, PyTorch, dbt, Airflow, Spark
**Posted:** 2026-07-07

> This strategic field engineering role helps APJ customers design, validate, and adopt Snowflake AI and ML solutions through technical discovery, proofs of concept, demonstrations, and executive readouts. It requires extensive machine learning and generative AI deployment experience, strong Python expertise, and executive-level presentation skills.

## Job Description

## Responsibilities
- Serve as the technical expert positioning Snowflake’s AI and ML features and value to technical stakeholders at customer organizations across the APJ region.
- Partner with account teams and customer champions to scope and drive proof-of-concepts to successful technical wins, including executive readouts and business value cases.
- Collaborate with product and engineering teams to influence AI and ML roadmaps based on customer feedback.
- Publish scalable content such as blog posts, conference presentations, technical collateral, notebooks, and demos.
- Influence, tailor, and maintain Sales Engineering AI and ML selling assets, including customer presentations, demonstrations, and customer stories.
- Help customers across the APJ region adopt AI and ML use cases on Snowflake.

## Requirements
- 10+ years of experience building and deploying machine learning solutions.
- 3+ years of experience building and deploying generative AI solutions in the cloud.
- Practitioner-level hands-on experience with machine learning workloads, including MLOps, feature stores, model deployment, model explainability, and observability.
- Familiarity with generative AI techniques such as retrieval-augmented generation (RAG), few-shot learning, prompt engineering, and fine-tuning.
- Experience operationalizing enterprise AI use cases such as interactive chat applications and text processing.
- Deep knowledge of Python and common machine learning packages, including LangChain, pandas, scikit-learn, and PyTorch.
- Experience with data engineering tools and technologies such as dbt, Airflow, and Spark.
- Strong presentation skills for technical and executive audiences, including whiteboarding sessions, formal readouts, and demos.
- Bachelor’s degree required; a master’s degree in computer science, engineering, mathematics, or a related field, or equivalent experience, preferred.

## Nice-to-haves
- Working knowledge of LLM ecosystem tools such as LangChain, LlamaIndex, or other open-source packages.
- Experience with large-scale infrastructure-as-a-service platforms such as AWS, Microsoft Azure, or Google Cloud.
- At least 1 year of practical Snowflake experience.
- Knowledge of large-scale database technologies such as Snowflake, Netezza, Exadata, Teradata, or Greenplum.
- Previous experience in quota-carrying roles.

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