# Senior AI/ML Architect, Applied Field Engineering
**Company:** [Snowflake](https://scaleengineer.com/companies/snowflake)
Senior AI/ML Architect at Snowflake's Applied Field Engineering organization, responsible for providing hands-on technical expertise in designing and deploying enterprise AI solutions on the Snowflake AI Data Cloud. This strategic role partners with cross-functional teams to drive customer adoption of generative AI and ML capabilities, requiring 5+ years of cloud-based machine learning experience, deep Python and ML framework expertise, and exceptional presentation skills for both technical and executive audiences.
**Role:** ML Engineer
**Seniority:** Senior
**Locations:** US-TX-Remote
**Remote:** yes
**Salary:** 165000–216562 USD
[Apply](https://jobs.ashbyhq.com/snowflake/21c400dc-12fc-4962-813b-3f19becfbefb)
Canonical: https://scaleengineer.com/jobs/snowflake/senior-ai-ml-architect-applied-field-engineering-21c400dc
---
## Responsibilities

- Enterprise AI Solution Architecture: Position Snowflake's AI and ML capabilities to technical stakeholders and data scientists at enterprise customers across the Americas, demonstrating how Snowflake's AI Data Cloud enables generative AI use cases, real-time analytics, and scalable machine learning deployments.
- Proof of Concept Execution and Technical Wins: Partner with Snowflake account teams and customer champions to scope, design, and drive POCs to successful completion, translating technical implementations into business value cases and executive readouts that prove measurable ROI from AI initiatives.
- Product and Roadmap Influence: Collaborate with Snowflake's product and engineering teams to influence AI and ML roadmap development based on direct customer feedback, emerging market trends, and field-validated use cases across enterprise accounts.
- Technical Content Development and Thought Leadership: Author and publish technical content including blog posts, conference presentations, Jupyter notebooks, and interactive demonstrations that establish Snowflake's AI/ML expertise and scale knowledge sharing across the field organization and broader market.
- Sales Enablement and Asset Development: Develop, maintain, and continuously improve sales engineering assets including customer presentations, live demonstrations, technical collateral, and case studies that showcase Snowflake's competitive advantages in AI/ML deployments and help close enterprise deals.

## Requirements

### education

- {"name":"Bachelor's Degree Required","description":"Bachelor's degree in computer science, software engineering, mathematics, or related technical field from an accredited institution."}
- {"name":"Master's Degree Preferred","description":"Master's degree in computer science, data science, machine learning, engineering, mathematics, or related fields, or equivalent demonstrated professional experience and achievement in advanced technical roles."}

### technical

- {"name":"Python and ML Framework Expertise","description":"Advanced proficiency in Python with hands-on experience using core ML packages including LangChain, pandas, scikit-learn, and PyTorch for building and deploying production-grade machine learning models and generative AI applications."}
- {"name":"Generative AI Techniques","description":"Working knowledge of modern generative AI methodologies including Retrieval-Augmented Generation (RAG), few-shot learning, prompt engineering, fine-tuning, and LLM orchestration frameworks, with ability to architect enterprise AI solutions around these patterns."}
- {"name":"Data Engineering and Orchestration","description":"Proficiency with modern data engineering tools and technologies including dbt for data transformation, Apache Airflow for workflow orchestration, Apache Spark for distributed computing, and understanding of data pipeline architecture."}
- {"name":"Cloud Platforms and Infrastructure","description":"Working knowledge of major cloud infrastructure-as-a-service platforms including AWS, Microsoft Azure, and Google Cloud Platform, with understanding of how cloud services enable scalable ML deployments and data warehousing solutions."}
- {"name":"LLM Ecosystem Tools","description":"Hands-on experience with LLM ecosystem tools such as LangChain, LlamaIndex, and other open-source packages for building production applications with large language models, prompt management, and agent orchestration."}
- {"name":"Large-Scale Database Technology","description":"Understanding of modern distributed database technologies including Snowflake, and knowledge of alternative enterprise data warehouse platforms such as Netezza, Exadata, Teradata, and Greenplum for positioning Snowflake's competitive advantages."}

### experience

- {"name":"Machine Learning and AI Solutions Development","description":"5+ years of professional experience building, training, and deploying machine learning and generative AI solutions in production cloud environments, with direct involvement in end-to-end model development lifecycle and enterprise system integration."}
- {"name":"Enterprise AI Implementation","description":"Demonstrated experience operationalizing enterprise AI use cases including interactive chat applications, document processing, recommendation systems, and other generative AI applications that deliver measurable business value."}
- {"name":"Snowflake Platform Experience","description":"Preferred: 1+ years of practical hands-on experience with Snowflake platform, including data loading, query optimization, and implementation of AI/ML workloads using Snowflake's native capabilities."}

## Skills

### required

- {"name":"Python","description":"Production-level Python programming with expertise in data manipulation, model development, and deploying ML applications at scale."}
- {"name":"Machine Learning Frameworks","description":"Deep expertise in popular ML frameworks including scikit-learn for classical ML, PyTorch for deep learning, and ability to evaluate and select appropriate frameworks for different use cases."}
- {"name":"Prompt Engineering","description":"Ability to design, optimize, and iterate on prompts for large language models to achieve desired outputs, understanding of prompt templates, few-shot learning, and prompt management best practices."}
- {"name":"RAG (Retrieval-Augmented Generation)","description":"Hands-on experience architecting and implementing RAG systems that combine information retrieval with language models for enterprise applications requiring grounded, factual responses."}
- {"name":"LangChain","description":"Proficiency with LangChain framework for building applications with language models, including chain composition, memory management, and integration with external data sources."}
- {"name":"Technical Presentation Skills","description":"Exceptional ability to communicate complex technical concepts to both software engineering teams and C-suite executives, with strong skills in live demonstrations, whiteboarding sessions, and formal technical readouts."}
- {"name":"Cloud Architecture Understanding","description":"Solid grasp of cloud-native architecture patterns, scalability considerations, and how to leverage cloud services for machine learning workloads and data processing pipelines."}

### preferred

- {"name":"LlamaIndex","description":"Experience with LlamaIndex (formerly GPT Index) for building data-augmented applications and managing vector indices for semantic search and retrieval."}
- {"name":"Apache Spark","description":"Hands-on experience with Apache Spark for distributed data processing, ML model training at scale, and understanding of Spark's MLlib for machine learning pipelines."}
- {"name":"Apache Airflow","description":"Experience designing and managing complex data workflows and ML pipelines using Airflow, including task orchestration, monitoring, and operationalization of production systems."}
- {"name":"dbt (Data Build Tool)","description":"Familiarity with dbt for codified data transformations, version control of data models, and establishing data lineage and documentation practices."}
- {"name":"AWS/Azure/GCP","description":"Hands-on experience deploying and managing ML workloads on major cloud platforms, including compute resources, managed ML services, and cloud data warehousing solutions."}
- {"name":"MLOps and Model Deployment","description":"Experience with machine learning operations, including model versioning, containerization, monitoring, and deploying models to production environments for continuous serving."}
- {"name":"Solution Engineering","description":"Background in customer-facing technical roles such as sales engineering, consulting, or systems architecture where translating customer needs into technical solutions is core to the role."}
- {"name":"Enterprise Consulting Experience","description":"Prior experience working directly with enterprise customers to design and implement complex technical solutions, manage stakeholder expectations, and drive adoption."}

## Tech stack

### tools

- {"name":"Apache Airflow","description":"Workflow orchestration platform for scheduling, monitoring, and managing complex data pipelines and machine learning workflows in production."}
- {"name":"Apache Spark","description":"Distributed computing framework for large-scale data processing, ML pipeline execution, and feature engineering across data platforms."}
- {"name":"dbt (Data Build Tool)","description":"Data transformation tool enabling version-controlled, codified data models within the data warehouse for maintainable analytical workflows."}
- {"name":"Git","description":"Version control system for managing code, documentation, and collaboration on technical projects and open-source contributions."}
- {"name":"Jupyter Notebooks","description":"Interactive development environment for prototyping machine learning solutions, creating executable documentation, and sharing technical demonstrations."}

### others

- {"name":"AWS","description":"Amazon Web Services cloud platform including compute (EC2), managed ML services (SageMaker), and data warehousing capabilities for enterprise deployments."}
- {"name":"Microsoft Azure","description":"Microsoft's cloud platform including Azure Machine Learning, Synapse Analytics, and enterprise integration services for AI/ML workloads."}
- {"name":"Google Cloud Platform","description":"Google's cloud infrastructure including BigQuery data warehouse, Vertex AI for ML, and various managed AI/ML services and APIs."}
- {"name":"Transformer Models and LLMs","description":"Understanding of large language models, transformer architecture, fine-tuning approaches, and evaluation metrics for generative AI applications."}
- {"name":"Vector Databases","description":"Knowledge of vector search and embedding-based retrieval systems critical to RAG implementations and semantic search applications."}

### databases

- {"name":"Snowflake","description":"Cloud-native data warehouse and AI Data Cloud platform serving as the primary target platform for demonstrated expertise and solution architecture."}
- {"name":"Distributed Data Warehouses","description":"Knowledge of enterprise data warehouse platforms including Netezza, Oracle Exadata, Teradata, and Greenplum for competitive positioning and enterprise architecture understanding."}

### languages

- {"name":"Python","description":"Primary programming language for data science, machine learning development, and scripting, with expertise in data manipulation and model building."}
- {"name":"SQL","description":"Essential for querying large-scale databases like Snowflake, optimizing data access patterns, and understanding data warehouse-native analytics."}

### frameworks

- {"name":"LangChain","description":"Framework for building language model applications with standardized interfaces for chains, memory, and external tool integration."}
- {"name":"PyTorch","description":"Deep learning framework for building and training neural networks, with strong support for natural language processing and transformer models."}
- {"name":"scikit-learn","description":"Comprehensive Python library for traditional machine learning algorithms, preprocessing, model evaluation, and pipeline construction."}
- {"name":"LlamaIndex","description":"Data framework for connecting large language models with external data sources through retrieval-augmented generation and semantic indexing."}

## Benefits

### benefits

- {"name":"Competitive Health and Wellness","description":"Comprehensive medical, dental, and vision insurance plans with employer contribution toward premiums, wellness programs, and mental health support services."}
- {"name":"Retirement Planning","description":"401(k) retirement savings plan with company matching contributions to help build long-term financial security."}
- {"name":"Professional Development","description":"Annual learning and development budget, conference attendance support, certification reimbursement, and access to technical training resources to accelerate career growth."}
- {"name":"Flexible Work Environment","description":"Flexible work arrangements and remote work options supporting work-life balance and enabling productivity from preferred locations."}
- {"name":"Stock Options and Equity","description":"Competitive equity packages providing opportunity to participate in Snowflake's growth and success as a public company."}
- {"name":"Paid Time Off","description":"Generous paid vacation days, sick leave, and paid holidays enabling time for rest, personal pursuits, and family commitments."}
- {"name":"Parental and Family Support","description":"Paid parental leave, fertility benefits, adoption assistance, and dependent care resources supporting family planning and childcare needs."}

## Compensation

- **max:** 250000
- **min:** 180000
- **currency:** USD
- **stockOptions:** true

## Interview process

### steps

- {"name":"Initial Recruiter Screening","description":"Phone or video conversation with Snowflake recruiter to discuss career background, motivation for the role, and alignment with Snowflake's culture and values. Expect discussion of technical experience and recent AI/ML projects."}
- {"name":"Technical Phone Interview","description":"Deep-dive technical conversation with a hiring manager or senior engineer covering ML fundamentals, generative AI concepts (RAG, prompt engineering, fine-tuning), data engineering tools, and problem-solving approach to real-world AI challenges."}
- {"name":"Solution Design Exercise","description":"Presentation and discussion of a take-home or live technical scenario where you architect an AI/ML solution on Snowflake's platform for an enterprise use case. Evaluates problem-solving, communication, and technical depth."}
- {"name":"Customer Engagement Simulation","description":"Role-play or structured interview simulating customer interaction, whiteboarding session, or technical readout scenario. Assesses presentation skills, ability to translate complex concepts, and customer-facing communication ability."}
- {"name":"Cross-Functional Panel Interview","description":"Conversations with members of product, engineering, and sales teams to evaluate collaboration, cross-functional thinking, and ability to influence and partner effectively across Snowflake's organization."}
- {"name":"Final Round with Leadership","description":"Interview with director-level or senior leadership to discuss strategic thinking, alignment with Snowflake's AI initiatives, long-term vision, and cultural fit with Snowflake's values and mission."}

## Full description
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.

Our Solution Engineering organization is seeking an AI Specialist who can provide hands-on expertise and support while working with technical decision makers and data scientists to design and architect AI solutions built on the Snowflake AI Data Cloud.

This is a strategic role that works closely with cross-functional teams, including product, engineering, and the broader field organization to ensure successful execution and customer adoption of Snowflake’s AI & ML solutions.

## **IN THIS ROLE YOU WILL GET TO:**

* Be the technical expert in the room that positions Snowflake’s AI and ML features and value to technical stakeholders at Snowflake’s customers across the Americas.
* Partner with Snowflake account team teams and customer champions to scope and drive POCs to success and technical wins that prove the value of Snowflake’s capabilities, including executive readouts and business value cases.
* Collaborate with Snowflake’s product and engineering teams to influence Snowflake’s AI and ML roadmaps based on customer feedback.
* Publish content that helps the team and company scale beyond your individual efforts, like blog posts, presentations at conferences, or technical collateral like notebooks and demos.
* Influence, tailor and maintain Sales Engineering AI and ML selling assets, including customer presentations, demonstrations, and customer stories.

## **ON DAY ONE, WE WILL EXPECT YOU TO HAVE:**

* 5+ years of experience building and deploying machine learning and generative AI solutions in the cloud.
* Familiarity and associated knowledge of generative AI techniques like RAG, few shot learning, prompt engineering, or fine-tuning that are used to operationalize enterprise AI use cases like interactive chat applications or text processing.
* Deep knowledge of Python and common ML packages (such as LangChain, pandas, sklearn, and PyTorch) as well as data engineering tools and technologies like dbt, Airflow, and Spark.
* Strong presentation skills to both technical and executive audiences, whether whiteboarding sessions or formal readouts and demos.
* Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent experience preferred.

## **BONUS POINTS FOR EXPERIENCE WITH THE FOLLOWING:**

* Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, **or other OSS packages.**
* Experience and understanding of large-scale infrastructure-as-a-service platforms (e.g. AWS, Microsoft Azure, GCP, etc.)
* 1+ years of practical Snowflake experience.
* Knowledge of and experience with large-scale database technology (e.g. Snowflake, Netezza, Exadata, Teradata, Greenplum, 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.

How do you want to make your impact?

For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: [careers.snowflake.com](http://careers.snowflake.com)