# Senior AI/ML Architect, Applied Field Engineering
**Company:** [Snowflake](https://scaleengineer.com/companies/snowflake)
Senior AI/ML Architect at Snowflake's Solution Engineering organization, this strategic role focuses on architecting enterprise AI solutions on the Snowflake AI Data Cloud while partnering with technical stakeholders, driving proof-of-concepts, and influencing product roadmaps. Candidates must bring 5+ years of cloud-native machine learning and generative AI development experience, deep Python and ML framework expertise, and exceptional presentation skills for both technical and executive audiences. This is an ideal opportunity for experienced ML engineers seeking to combine technical depth with strategic business impact in the rapidly evolving enterprise AI landscape.
**Role:** ML Engineer
**Seniority:** Senior
**Locations:** US-IL-Chicago-MSO
**Salary:** 165000–216562 USD
[Apply](https://jobs.ashbyhq.com/snowflake/9d538c1b-16fa-4d21-8549-d1d1993aa201)
Canonical: https://scaleengineer.com/jobs/snowflake/senior-ai-ml-architect-applied-field-engineering-9d538c1b
---
## Responsibilities

- Technical Leadership & Solution Architecture: Serve as the technical expert and strategic advisor positioning Snowflake's AI and ML capabilities to technical stakeholders, data scientists, and C-level executives across the Americas. Architect end-to-end AI solutions on the Snowflake AI Data Cloud, translating complex business requirements into scalable, production-ready implementations that demonstrate measurable business value and technical innovation.
- Proof-of-Concept Development & Execution: Partner with Snowflake account teams and customer champions to scope, design, and drive proof-of-concepts to successful completion. Develop technical wins that validate Snowflake's AI/ML capabilities, including creating executive readouts, building business value cases, and establishing quantifiable success metrics that accelerate customer adoption and deal closure.
- Product Roadmap Influence & Customer Advocacy: Collaborate with Snowflake's product and engineering teams to gather comprehensive customer feedback and technical insights from the field. Translate customer pain points and emerging use cases into actionable product recommendations that shape Snowflake's AI and ML roadmap, ensuring the platform evolves with enterprise market demands.
- Content Creation & Thought Leadership: Develop scalable technical content including blog posts, technical notebooks, interactive demos, and conference presentations that extend your impact beyond individual customer engagements. Create and maintain a portfolio of reusable sales engineering assets, customer presentations, case studies, and demonstrations that empower the broader field organization to deliver consistent technical value.
- Sales Engineering Enablement & Asset Development: Influence, customize, and maintain the Solution Engineering team's AI and ML selling assets and collateral. Develop technical presentations, live demonstrations, and customer success stories that showcase Snowflake's competitive advantages in the enterprise AI market, ensuring field teams have the tools to engage customers effectively.
- Cross-Functional Collaboration & Alignment: Work closely with product, engineering, and field organizations to ensure coordinated execution of AI/ML initiatives. Serve as a liaison between customer requirements and internal development teams, identifying opportunities to scale successful patterns and drive organizational alignment around technical strategy.

## Requirements

### education

- {"name":"Bachelor's Degree (Required)","description":"Bachelor's degree from an accredited institution in any discipline is required for this role."}
- {"name":"Master's Degree (Preferred)","description":"Master's degree in Computer Science, Software Engineering, Mathematics, Statistics, or related technical fields strongly preferred. Advanced degree holders typically bring deeper theoretical foundations in ML algorithms, statistical methods, and systems design that accelerate impact in this architectural role."}
- {"name":"Equivalent Professional Experience","description":"Candidates without advanced degrees can substitute equivalent professional experience demonstrating mastery of ML engineering, AI architecture, and cloud platform expertise through documented project portfolios and proven technical contributions."}

### technical

- {"name":"Machine Learning & Generative AI Architecture","description":"5+ years of hands-on experience designing and deploying production machine learning and generative AI solutions in cloud environments. Proficiency in operationalizing enterprise AI use cases such as interactive chat applications, text processing systems, and intelligent automation workflows at scale."}
- {"name":"Generative AI Techniques & LLM Engineering","description":"Deep expertise in generative AI methodologies including Retrieval-Augmented Generation (RAG), few-shot learning, advanced prompt engineering, and fine-tuning techniques. Practical experience implementing these techniques in production systems and understanding their trade-offs for enterprise applications."}
- {"name":"Python & Machine Learning Frameworks","description":"Expert-level proficiency in Python with extensive experience using core ML libraries including pandas for data manipulation, scikit-learn for classical ML algorithms, PyTorch for deep learning, and LangChain for LLM application development. Ability to write production-quality, maintainable code that bridges data science and engineering."}
- {"name":"Data Engineering & Orchestration","description":"Strong knowledge of modern data engineering tools and technologies including dbt for data transformation, Apache Airflow for workflow orchestration, and Apache Spark for distributed computing. Understanding of data pipelines, ETL processes, and how AI/ML integrates with enterprise data architectures."}
- {"name":"Cloud Data Platforms & Infrastructure","description":"Experience with large-scale Infrastructure-as-a-Service platforms, particularly AWS, Microsoft Azure, or Google Cloud Platform. Familiarity with cloud-native architecture patterns, deployment strategies, and cost optimization principles specific to ML workloads and data processing at enterprise scale."}
- {"name":"Large-Scale Database Technology","description":"Working knowledge of enterprise data warehouse and analytical database platforms such as Snowflake, Netezza, Oracle Exadata, Teradata, or Greenplum. Understanding of how modern cloud data platforms enable AI/ML workflows and the architectural patterns for integrating AI solutions with enterprise data infrastructure."}

### experience

- {"name":"Cloud ML & AI Solution Development","description":"Minimum 5+ years of professional experience building, deploying, and operating machine learning and generative AI solutions in production cloud environments, demonstrating progression from individual contributor to architect-level responsibilities."}
- {"name":"Enterprise AI Implementation","description":"Direct experience implementing enterprise-grade AI solutions including chatbots, intelligent document processing, recommendation systems, or other business-critical applications. Evidence of impact through successful POCs, production deployments, or customer implementations."}
- {"name":"Snowflake Platform Experience (Bonus)","description":"1+ years of hands-on experience with Snowflake, including SQL development, performance optimization, integration with Python/ML workflows, or data application development. Familiarity with Snowflake's data sharing, governance, and AI capabilities is highly valued."}
- {"name":"LLM Ecosystem & OSS Contributions","description":"Working knowledge of tools within the LLM ecosystem beyond core libraries, including LLamaIndex, Hugging Face libraries, or other open-source packages. Experience integrating multiple LLM frameworks and understanding ecosystem evolution is a bonus."}

## Skills

### required

- {"name":"Python Programming","description":"Expert-level Python development with strong software engineering practices, code quality standards, and ability to write scalable ML pipelines and data processing applications."}
- {"name":"Machine Learning Engineering","description":"End-to-end ML expertise including problem formulation, feature engineering, model training, evaluation, optimization, and production deployment. Deep understanding of ML workflows, experimentation frameworks, and MLOps practices."}
- {"name":"Generative AI & LLM Applications","description":"Practical expertise building applications with Large Language Models including prompt optimization, RAG systems, fine-tuning strategies, and evaluation metrics for generative AI outputs in production environments."}
- {"name":"Data Engineering & SQL","description":"Strong SQL skills and familiarity with modern data engineering practices including data pipelines, transformations, orchestration, and integration of data with ML workflows. Understanding of data quality, governance, and lineage."}
- {"name":"Cloud Platform Expertise","description":"Hands-on experience with AWS, Azure, or GCP including deploying ML models, managing compute resources, understanding pricing and cost optimization, and architecting scalable solutions on cloud infrastructure."}
- {"name":"Technical Communication & Presentation","description":"Exceptional ability to communicate complex AI/ML concepts to diverse audiences including C-suite executives, technical teams, and business stakeholders. Skilled at whiteboarding sessions, technical demos, formal presentations, and creating compelling business value narratives."}
- {"name":"Solution Architecture & Design","description":"Ability to translate customer requirements into scalable technical architecture, considering trade-offs between performance, cost, maintainability, and business objectives. Experience designing end-to-end solutions leveraging modern data platforms and AI services."}

### preferred

- {"name":"LLamaIndex & Advanced RAG Patterns","description":"Experience with LLamaIndex or similar frameworks for building sophisticated retrieval-augmented generation systems, including multi-modal indexing, hybrid retrieval strategies, and advanced prompt routing patterns."}
- {"name":"Apache Airflow Orchestration","description":"Hands-on experience designing and maintaining Apache Airflow DAGs for complex ML workflows, data pipelines, and production scheduling at scale. Understanding of task dependencies, failure handling, and monitoring."}
- {"name":"dbt Data Transformation","description":"Experience using dbt for data modeling and transformation, particularly in contexts where ML pipelines consume dbt-processed data. Understanding of how modern data tools integrate with ML workflows."}
- {"name":"PyTorch Deep Learning","description":"Practical experience with PyTorch including model architecture design, training optimization, transfer learning, and fine-tuning strategies for deep learning applications including transformer models."}
- {"name":"Vector Databases & Semantic Search","description":"Experience implementing vector similarity search, embedding models, and vector database platforms like Pinecone, Weaviate, or Milvus in production applications, particularly for RAG and semantic search use cases."}
- {"name":"A/B Testing & Experimentation","description":"Expertise in designing and executing rigorous A/B tests and experimentation frameworks for ML model evaluation, feature validation, and business impact measurement in production systems."}
- {"name":"Customer-Facing Technical Leadership","description":"Previous experience in sales engineering, solution architecture, or customer advisory roles where technical expertise was leveraged to influence customer decisions and drive adoption of sophisticated technology solutions."}
- {"name":"Industry Knowledge & Enterprise Compliance","description":"Familiarity with enterprise governance, data security, regulatory compliance (HIPAA, GDPR, SOC 2), and responsible AI practices. Understanding of how these considerations impact ML architecture decisions in regulated industries."}

## Tech stack

### tools

- {"name":"Apache Airflow","description":"Leading workflow orchestration platform for scheduling, monitoring, and managing complex ML pipelines and data engineering workflows at enterprise scale."}
- {"name":"Apache Spark","description":"Distributed computing framework for large-scale data processing and feature engineering. Critical for processing massive datasets in ML preparation phases and distributed training scenarios."}
- {"name":"dbt (Data Build Tool)","description":"Modern data transformation tool enabling version-controlled, tested SQL-based data modeling. Bridges gap between data engineering and analytics in AI-ready data architectures."}
- {"name":"Jupyter Notebooks","description":"Interactive development environment for ML experimentation, prototyping, and technical communication. Essential tool for both development and creating executable documentation for stakeholders."}
- {"name":"Git & Version Control","description":"Essential for collaborative development, code management, and maintaining reproducibility in ML projects. Critical for managing ML models, data pipelines, and infrastructure-as-code."}

### others

- {"name":"AWS, Azure, or GCP","description":"Cloud infrastructure platforms for deploying and scaling ML applications, managing compute resources, and integrating with managed AI services. Deep understanding of cloud architecture patterns essential."}
- {"name":"REST APIs & Microservices Architecture","description":"Experience designing and implementing APIs that serve ML models in production, understanding latency requirements, scaling patterns, and integration with enterprise systems."}
- {"name":"Docker & Containerization","description":"Container technologies for packaging ML applications, ensuring reproducibility, and enabling scalable deployment across environments. Foundation for modern ML operations."}
- {"name":"MLOps & Model Monitoring","description":"Understanding of machine learning operations practices including model versioning, A/B testing frameworks, performance monitoring, drift detection, and continuous improvement cycles."}
- {"name":"Retrieval-Augmented Generation (RAG)","description":"Advanced technique combining large language models with information retrieval for contextually accurate AI applications. Critical architecture pattern for enterprise generative AI solutions."}

### databases

- {"name":"Snowflake","description":"Cloud-native data warehouse and platform for AI/ML workloads. Primary platform for this role, combining SQL analytics with AI capabilities including Cortex AI services and native Python integration."}
- {"name":"Vector Databases","description":"Specialized databases for storing and querying high-dimensional embeddings. Essential infrastructure for RAG systems and semantic search applications powering modern generative AI solutions."}
- {"name":"PostgreSQL","description":"Reliable relational database often used in ML architectures for metadata storage, application state, and transactional workloads complementing analytical data platforms."}

### languages

- {"name":"Python","description":"Primary programming language for machine learning development, data processing, and AI solution implementation. Required for building and maintaining production ML systems."}
- {"name":"SQL","description":"Essential for data querying, analysis, and integration with data warehousing platforms like Snowflake. Critical for understanding data lineage and optimizing data pipelines for ML workflows."}

### frameworks

- {"name":"LangChain","description":"Leading framework for building LLM applications including chains, agents, and retrieval-augmented generation systems. Core tool for operationalizing generative AI use cases in enterprise environments."}
- {"name":"PyTorch","description":"Industry-standard deep learning framework used for building and fine-tuning neural networks, including transformer models and custom ML architectures. Essential for production ML systems."}
- {"name":"scikit-learn","description":"Foundational ML library providing classical algorithms for classification, regression, clustering, and feature engineering. Critical for building comprehensive ML solutions combining classical and deep learning approaches."}
- {"name":"pandas","description":"Essential data manipulation and analysis library. Primary tool for exploratory data analysis, data cleaning, and feature engineering in ML pipelines."}
- {"name":"LLamaIndex","description":"Advanced framework for building retrieval-augmented generation systems with support for complex indexing strategies, multi-modal data, and sophisticated query processing against large document corpora."}

## Benefits

### benefits

## Compensation

- **max:** 0
- **min:** 0
- **currency:** 
- **stockOptions:** false

## Interview process

### steps

## 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)