> Markdown version of [/jobs/ext/2631501-advanced-professional-handling-complex-enterprise-ai-ml-deployments](https://www.wearedevelopers.com/jobs/ext/2631501-advanced-professional-handling-complex-enterprise-ai-ml-deployments). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# advanced professional handling complex enterprise AI/ML deployments

- **Company:** Snowflake Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, ARM Architecture, Microsoft Azure, Extract Transform Load (ETL), Data Security, Distributed Systems, Python (Programming Language), Machine Learning, Cloud Services, Google Cloud, Enterprise Software Applications, Feature Engineering, Large Language Models, Snowflake, Apache Spark, Technical Debt, Kubernetes, Machine Learning Operations, Data Pipelines, Databricks
- **Published:** August 28, 2026
- **Apply:** https://www.dice.com/job-detail/1b2cc2a9-d234-4fe0-88a8-039c59514125

## About the Role

* Experience with GenerativeAI, LLMs and Vector Databases.
 * Experience with Databricks/Apache Spark.
 * Experience implementing data pipelines using ETL tools.
 * Experience working in a Data Science role.
 * Proven success at enterprise software.
 * Vertical expertise in a core vertical such as FSI, Retail, Manufacturing, etc.

Snowflake is growing fast, and we're scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.

## Description

* Design robust, scalable AI/ML solutions utilizing the full Snowflake native stack and partner ecosystem.
 * Perform deep-dive Root Cause Analysis (RCA) for complex system dependencies in AI/ML solutions.
 * Collaborate cross-functionally with Sales and Product teams to align technical roadmaps with customer ROI.
 * Mentor Level 3 architects on best practices for MLOps and architectural design.

TECHNICAL DEPTH & RISK MANAGEMENT:

 * Distributed Systems: Deconstruct failures in complex pipelines involving external cloud services (AWS/Azure/Google Cloud Platform).
 * Predictive Failure Analysis: Critically think about potential failure modes like model drift and data skew early in the lifecycle.
 * Governance: Architect data security and access controls specifically for sensitive AI/ML training data.

SNOWFLAKE-NATIVE TECH STACK:

 * Snowflake Model Registry, Cortex Functions, Python, External MLOps (Kubeflow/SageMaker).

 * Reduction in post-deployment technical debt; Regional success of complex implementations; Peer mentorship impact.
 * Thorough understanding of the complete Data Science life-cycle including feature engineering, model development, model deployment and model management.


## Related Videos

- [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology)
- [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way)
- [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team)
- [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads)

## Related Articles

- [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud)
- [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)
- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)