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

Snowflake2 days ago

Location

SG-Singapore

Type

Full Time

Salary

USD 180,000 – 240,000

Level

Senior

Role

Senior AI/ML Architect

Posted

Jul 7, 2026

Full TimeSenior

The role

Summary

As a Senior AI/ML Architect in Applied Field Engineering at Snowflake, you will serve as the principal technical expert and Field CTO for the APJ region, architecting and deploying enterprise AI solutions on Snowflake's cloud data platform. This strategic role requires hands-on expertise in machine learning operations, generative AI systems, and cloud infrastructure, combined with exceptional communication skills to influence technical decision-makers and drive high-impact proof-of-concept engagements that demonstrate measurable business value.

What you'll do

Technical Leadership for AI/ML Solutions: Serve as the primary technical authority and solution architect for Snowflake's AI and ML capabilities across the APJ region. Position Snowflake's generative AI and machine learning features to technical stakeholders, data scientists, and enterprise decision-makers. Design comprehensive AI solutions leveraging Snowflake's AI Data Cloud platform, translating complex technical requirements into actionable architectural recommendations that address customer pain points and unlock business value.

Proof-of-Concept Design and Execution: Partner strategically with Snowflake's account teams and customer technical champions to scope, architect, and drive POC engagements to successful technical outcomes. Develop detailed solution blueprints incorporating best practices in machine learning operations, feature engineering, and model deployment. Present compelling executive readouts with quantified business value propositions and ROI analyses that demonstrate the tangible impact of Snowflake's AI capabilities on enterprise operations and decision-making.

Product Roadmap Influence and Customer Advocacy: Collaborate actively with Snowflake's product management and engineering teams to translate field customer feedback into actionable insights that shape the evolution of AI and ML product roadmaps. Identify emerging use cases, technical gaps, and market requirements through direct customer engagement. Advocate for feature enhancements and platform improvements that address real-world enterprise AI deployment challenges and competitive positioning requirements.

Technical Content Creation and Thought Leadership: Author and publish technical content including blog posts, white papers, technical deep-dive notebooks, and interactive demonstrations that establish Snowflake's AI expertise and scale organizational knowledge beyond individual contribution. Present at industry conferences, webinars, and customer forums on generative AI implementation, MLOps best practices, and enterprise AI strategy. Create reusable technical collateral and reference architectures that enable the broader sales and customer success teams to deliver consistent messaging and technical validation.

Sales Enablement and Competitive Positioning: Develop, curate, and maintain comprehensive sales engineering assets including customer-facing presentations, interactive product demonstrations, case studies, and technical reference materials. Tailor materials for diverse audiences spanning data engineers, data scientists, and C-suite executives. Ensure all sales collateral accurately reflects Snowflake's competitive differentiation in the AI and ML marketplace and demonstrates practical implementation pathways for enterprise customers.

Regional Customer Adoption and Implementation Support: Work cross-functionally with account teams spanning the APJ region to facilitate customer adoption of AI and ML use cases on Snowflake. Provide hands-on technical guidance during critical implementation phases, troubleshooting complex technical challenges related to model deployment, data pipeline optimization, and production ML systems. Drive adoption metrics and customer success outcomes through proactive technical support and advisory services.

What we look for

Technical

Machine Learning and Deep Learning Systems10+ years of professional experience architecting, building, and deploying production machine learning systems and deep learning models. Hands-on expertise in the complete ML lifecycle including feature engineering, model selection, hyperparameter tuning, cross-validation methodologies, and production deployment patterns. Proficiency with modern ML frameworks and ability to evaluate trade-offs between different modeling approaches for enterprise applications.

Generative AI and Large Language Models3+ years of demonstrated experience designing and deploying generative AI solutions in cloud environments. Practitioner-level knowledge of prompt engineering techniques, few-shot learning methodologies, and retrieval-augmented generation (RAG) architectures. Understanding of fine-tuning approaches, parameter-efficient adaptation methods, and techniques for operationalizing LLMs in production settings. Experience building conversational AI applications, text processing pipelines, and knowledge retrieval systems.

MLOps and Model Lifecycle ManagementDemonstrated proficiency in machine learning operations including experiment tracking, model versioning, automated testing frameworks, and continuous integration/continuous deployment (CI/CD) pipelines for ML systems. Experience with feature stores for centralized feature management, model serving infrastructure, and production monitoring. Knowledge of model governance, lineage tracking, and reproducibility best practices in enterprise environments.

Model Deployment, Explainability, and ObservabilityHands-on experience deploying machine learning models to production environments with emphasis on model interpretability, explainability frameworks, and monitoring strategies. Proficiency in evaluation methodologies for model performance, drift detection, and production model observability. Experience with techniques to ensure fairness, bias detection, and regulatory compliance in deployed AI systems. Knowledge of model explainability libraries and approaches for building trust in AI-driven business decisions.

Python Programming and ML EcosystemAdvanced proficiency in Python for data science and machine learning applications. Deep working knowledge of core ML packages including scikit-learn for traditional ML, PyTorch or TensorFlow for deep learning, pandas for data manipulation, NumPy for numerical computing, and LangChain for LLM orchestration. Ability to write production-quality Python code following software engineering best practices, code organization patterns, and documentation standards applicable to data science workflows.

Data Engineering and Pipeline ArchitectureSolid understanding of modern data engineering tools and technologies including dbt for data transformation, Apache Airflow for workflow orchestration, and Apache Spark for large-scale distributed data processing. Experience designing data pipelines that support ML use cases, implementing data quality checks, and optimizing data ingestion and feature engineering workflows. Knowledge of data warehouse optimization, query performance tuning, and data governance practices relevant to AI and ML workloads.

Cloud Platform InfrastructureProficiency with major cloud infrastructure-as-a-service platforms including AWS (SageMaker, EC2, Lambda, RDS), Microsoft Azure (ML Services, Databricks), or Google Cloud Platform (Vertex AI, BigQuery). Understanding of cloud networking, security models, identity and access management, and cost optimization strategies. Experience deploying ML models across cloud environments and leveraging managed services for scalable ML infrastructure.

Snowflake Platform Expertise (Preferred)1+ years of hands-on experience with Snowflake as a data warehouse and analytics platform. Proficiency with Snowflake's SQL dialect, data warehousing concepts, and optimization techniques. Understanding of Snowflake's ecosystem including support for Python, integration with ML frameworks, and emerging AI capabilities. Familiarity with Snowflake best practices for data organization, performance tuning, and cost management.

Large-Scale Database TechnologyWorking knowledge and operational experience with enterprise-grade database technologies including Snowflake, Teradata, Oracle Exadata, Netezza, or Greenplum. Understanding of distributed query processing, columnar storage architectures, query optimization, and massively parallel processing (MPP) environments. Experience tuning complex SQL workloads and optimizing analytical queries for performance and cost efficiency.

LLM Ecosystem and Open-Source FrameworksWorking knowledge of the broader LLM and generative AI ecosystem including open-source frameworks such as LangChain for LLM orchestration, LlamaIndex for vector storage and retrieval, Hugging Face Transformers for model access, and community-driven packages for AI applications. Familiarity with model hubs, pre-trained model libraries, and techniques for leveraging OSS implementations in enterprise solutions.

Education

Bachelor's Degree RequiredBachelor's degree in Computer Science, Computer Engineering, Software Engineering, or related technical discipline required as baseline qualification. Equivalent professional experience in building and shipping production software systems may substitute for formal degree requirements in some cases.

Master's Degree PreferredMaster's degree in Computer Science, Engineering, Mathematics, Statistics, Data Science, or related field is highly preferred and demonstrates advanced theoretical knowledge applicable to complex ML systems. Graduate-level coursework in machine learning, artificial intelligence, statistical methods, and advanced algorithms provides valuable foundation for this architecture-level role.

Experience

Machine Learning Engineering Track Record10+ years of progressive professional experience in machine learning engineering, data science, or AI systems development. Demonstrated track record building and deploying machine learning solutions in production environments serving millions of end-users or processing terabyte-scale datasets. Experience across multiple industries and domains showcasing versatility in applying ML to diverse business problems.

Generative AI Implementation Experience3+ years of hands-on experience designing, implementing, and deploying generative AI solutions in enterprise cloud environments. Proven ability to take generative AI from proof-of-concept to production, addressing challenges related to model fine-tuning, retrieval strategies, prompt optimization, and cost management at scale. Direct experience with customer deployments or enterprise implementations.

Technical Leadership and Sales EngineeringDemonstrated experience in technical sales engineering, field engineering, or solutions architecture roles requiring translation of complex technical concepts for non-technical audiences. Track record of driving high-value customer engagements, leading technical evaluations, and influencing purchasing decisions through compelling technical demonstrations and proof-of-concept execution. Prior quota-carrying sales engineering experience is a bonus indicating strong commercial acumen.

Strategic Customer EngagementProven ability to work collaboratively with enterprise customers at multiple organizational levels from technical practitioners to executive stakeholders. Experience scoping and executing large technical projects with measurable business outcomes. Demonstrated skill in translating customer business requirements into technical architectures and communicating value propositions across business and technical stakeholders.

Skills

Required skills

Machine Learning Architecture and DesignExpert-level ability to design end-to-end machine learning systems including data pipeline design, feature engineering strategies, model architecture selection, and production deployment patterns. Proficiency in evaluating trade-offs between different modeling approaches and recommending optimal solutions for specific business problems and technical constraints.

Generative AI and LLM DeploymentDeep hands-on experience with generative AI systems, large language models, and transformer-based architectures. Proficiency in retrieval-augmented generation (RAG), prompt engineering at scale, fine-tuning methodologies, and production optimization techniques for reducing model inference costs and latency. Experience building and deploying conversational AI applications.

Advanced Python ProgrammingAdvanced Python skills for data science and ML applications with proficiency in ecosystem packages (scikit-learn, PyTorch, TensorFlow, LangChain, pandas, NumPy). Ability to write well-structured, maintainable code following software engineering best practices. Experience building libraries, frameworks, or tools that demonstrate deep Python expertise.

Data Engineering and Pipeline OrchestrationSolid hands-on expertise in modern data engineering tools including dbt for data transformation, Apache Airflow for workflow orchestration, and Apache Spark for distributed data processing. Understanding of data quality frameworks, data lineage, and building reliable data pipelines that serve analytical and ML workloads.

Cloud Platform ML ServicesHands-on experience deploying machine learning systems on major cloud platforms (AWS, Azure, GCP). Proficiency with managed ML services including AWS SageMaker, Azure ML, or Google Vertex AI. Understanding of cloud infrastructure, networking, security, and cost optimization for ML workloads.

Technical Presentation and CommunicationExceptional ability to communicate complex technical concepts clearly to diverse audiences ranging from data scientists to C-level executives. Strong whiteboarding skills for technical design discussions. Demonstrated ability to deliver compelling technical presentations, product demonstrations, and executive business value readouts. Experience writing technical documentation and creating internal educational content.

MLOps and Production ML SystemsComprehensive understanding of ML operations including model versioning, experiment tracking, automated testing, CI/CD pipelines for ML, and production model monitoring. Experience with feature stores for managing training and serving features. Knowledge of model governance, reproducibility, and operational excellence in production ML environments.

Solution ArchitectureAbility to translate complex customer requirements and business problems into coherent technical architectures. Proficiency in creating architectural diagrams, solution blueprints, and implementation roadmaps. Experience evaluating technology choices, assessing trade-offs, and recommending optimal solutions for enterprise deployments.

Nice to have

Snowflake Platform ExpertiseHands-on experience with Snowflake data warehouse including data organization, SQL optimization, governance features, and integration with AI/ML tools. Understanding of Snowflake's architecture, performance characteristics, and optimization techniques. Familiarity with Snowflake's ecosystem and emerging AI capabilities.

Enterprise Data Warehouse TechnologyWorking knowledge of enterprise-scale database technologies including Teradata, Oracle Exadata, Netezza, or Greenplum. Understanding of distributed query processing, columnar storage, and MPP (massively parallel processing) architectures. Experience optimizing complex analytical queries and managing large-scale analytical workloads.

Open-Source LLM EcosystemWorking knowledge of open-source LLM tools and frameworks including LangChain, LlamaIndex, Hugging Face Transformers, and community-driven AI packages. Experience evaluating and integrating OSS libraries into enterprise solutions. Familiarity with model hubs and pre-trained model repositories.

Quota-Carrying Sales EngineeringPrior experience in quota-carrying sales engineering roles demonstrating ability to balance technical depth with commercial awareness. Track record of identifying customer opportunities, scoping deals, and driving technical evaluations toward successful outcomes. Understanding of sales processes and ability to partner effectively with account executives.

Multi-Cloud Deployment StrategiesExperience designing and deploying ML systems across multiple cloud providers (hybrid or multi-cloud approaches). Understanding of cloud portability, data residency requirements, and cost optimization across different cloud platforms. Knowledge of cloud-agnostic ML frameworks and deployment patterns.

Industry-Specific AI ApplicationsDeep experience applying AI and ML solutions to specific industry verticals such as financial services, healthcare, retail, manufacturing, or telecommunications. Understanding of industry-specific compliance requirements, data governance, and common use cases that can be leveraged for enterprise sales.

Thought Leadership and Public SpeakingTrack record of publishing technical content, speaking at industry conferences, participating in podcasts, or building personal brand as an AI/ML subject matter expert. Ability to influence technical communities and establish credibility through content creation and public speaking engagements.