The Role
Design and build scalable Snowflake-based AI infrastructure and data pipelines, optimize compute/cost/performance, lead architecture reviews, define CI/CD and MLOps practices, ensure security/compliance, mentor engineers, and drive vendor and technology selection for production AI workloads.
Summary Generated by Built In
Project Role : AI Infrastructure Architect
Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.
Must have skills : AI Agents & Workflow Integration
Good to have skills : Snowflake Data Warehouse
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
As a Senior Engineer in AI Infrastructure Architecture for Snowflake, you will own significant portions of the end-to-end architecture and engineering of optimized data and AI infrastructure for production machine learning and AI-enabled applications. You will design scalable warehouses, Snowpark workloads, AI-ready data/feature pipelines, model-enablement patterns, automation and operational controls that align with client standards, SLAs, security, compliance and cost-efficiency expectations. You will bring industry experience across enterprise AI adoption, platform modernization, regulated data workloads, FinOps and production reliability, while mentoring engineers and partnering with architects to translate business requirements into robust Snowflake-based AI infrastructure solutions.
Key Responsibilities
Own end-to-end architecture and design of optimized Snowflake data and AI infrastructure, including warehouses, Snowpark workloads, secure data architecture, AI-ready feature/data pipelines, model integration and AI application enablement.
Design and tune scalable Snowflake warehouses, tasks, streams, Snowpark services, Cortex/AI capabilities, Streamlit applications and cloud integrations, including compute sizing, query optimization, governance, access controls and high-throughput data access design.
Serve as an authoritative AI infrastructure expert on Snowflake, applying deep knowledge of Snowflake Data Cloud capabilities, data/AI application patterns, governance, security and cost levers.
Develop and evaluate architecture alternatives, weighing trade-offs across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost and operational complexity.
Lead architecture assessments and reviews of existing and proposed environments, identifying gaps, risks, bottlenecks and optimization opportunities, and recommending remediation actions.
Drive architecture decision-making by documenting rationale, trade-offs, assumptions and dependencies so decisions are transparent, defensible and aligned with business SLAs and standards.
Define and maintain AI infrastructure roadmap inputs, capacity planning models, scaling strategies, cost forecasts and performance improvement opportunities.
Design deployment, automation and CI/CD strategies for reliable, repeatable and scalable releases of AI systems, models, data pipelines and platform components into production.
Establish AI monitoring and observability practices across InfraOps and MLOps, including SLAs, SLOs, alerting, performance/cost tracking and continuous optimization.
Integrate AI/ML systems into enterprise environments while ensuring interoperability, security, compliance, regulatory alignment and adherence to client standards.
Collaborate with clients, stakeholders, architects and engineering teams to align infrastructure decisions with business outcomes and translate requirements into actionable architecture standards.
Set technical direction for workstreams, mentor engineers, review designs/code and promote engineering best practices across the team.
Required Qualifications
Bachelor's degree in Computer Science, Computer Engineering, Information Technology or a related engineering field.
Minimum 4 years of experience coding, building, monitoring, troubleshooting, designing and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines or large-scale engineering solutions.
Strong understanding of AI/ML concepts and the computing infrastructure required to deploy, run and optimize production AI workloads.
Minimum 4 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell or equivalent engineering languages.
Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services or platform-native workflow tooling.
Strong problem-solving skills and ability to work in a fast-paced engineering or client delivery environment.
Excellent communication, collaboration and stakeholder alignment skills.
Minimum 4 years of experience in AI/ML infrastructure engineering or related roles on a hyperscaler or enterprise platform for deploying large-scale solutions.
Proven experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives.
Demonstrated experience evaluating and selecting AI technologies, frameworks, cloud services and architecture patterns.
Required Skills/ Experience
Strong hands-on experience with Snowflake warehouses, databases, schemas, secure data sharing/access controls, Snowpark, Cortex/AI capabilities, Streams/Tasks, Streamlit and cloud ecosystem integrations.
Experience architecting scalable data and feature pipelines, AI application integrations, model-enablement patterns, governance, query/warehouse optimization and secure data access.
Strong working knowledge of SQL, Python, dbt/Terraform, CI/CD, DataOps, MLOps/AI enablement patterns, observability and incident response practices.
Ability to optimize Snowflake AI infrastructure for performance, cost, scalability, security, reliability and compliance.
Experience producing architecture decision records, reference implementations, standards, runbooks and reusable platform patterns.
Good to Have Skills
Snowflake certifications such as SnowPro Advanced Architect, SnowPro Advanced Data Engineer or related AI/data platform credentials.
Industry experience in BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy or public sector environments where data/AI platforms must meet compliance, security, reliability and cost-control requirements.
Exposure to Snowpark, Cortex/AI features, vector search, retrieval pipelines, feature engineering, model enablement and AI application architecture.
Knowledge of Snowflake governance, secure data sharing, FinOps, infrastructure partner/vendor collaboration and production support operating models.
15 years full time education
Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.
Must have skills : AI Agents & Workflow Integration
Good to have skills : Snowflake Data Warehouse
Minimum 12 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
As a Senior Engineer in AI Infrastructure Architecture for Snowflake, you will own significant portions of the end-to-end architecture and engineering of optimized data and AI infrastructure for production machine learning and AI-enabled applications. You will design scalable warehouses, Snowpark workloads, AI-ready data/feature pipelines, model-enablement patterns, automation and operational controls that align with client standards, SLAs, security, compliance and cost-efficiency expectations. You will bring industry experience across enterprise AI adoption, platform modernization, regulated data workloads, FinOps and production reliability, while mentoring engineers and partnering with architects to translate business requirements into robust Snowflake-based AI infrastructure solutions.
Key Responsibilities
Own end-to-end architecture and design of optimized Snowflake data and AI infrastructure, including warehouses, Snowpark workloads, secure data architecture, AI-ready feature/data pipelines, model integration and AI application enablement.
Design and tune scalable Snowflake warehouses, tasks, streams, Snowpark services, Cortex/AI capabilities, Streamlit applications and cloud integrations, including compute sizing, query optimization, governance, access controls and high-throughput data access design.
Serve as an authoritative AI infrastructure expert on Snowflake, applying deep knowledge of Snowflake Data Cloud capabilities, data/AI application patterns, governance, security and cost levers.
Develop and evaluate architecture alternatives, weighing trade-offs across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost and operational complexity.
Lead architecture assessments and reviews of existing and proposed environments, identifying gaps, risks, bottlenecks and optimization opportunities, and recommending remediation actions.
Drive architecture decision-making by documenting rationale, trade-offs, assumptions and dependencies so decisions are transparent, defensible and aligned with business SLAs and standards.
Define and maintain AI infrastructure roadmap inputs, capacity planning models, scaling strategies, cost forecasts and performance improvement opportunities.
Design deployment, automation and CI/CD strategies for reliable, repeatable and scalable releases of AI systems, models, data pipelines and platform components into production.
Establish AI monitoring and observability practices across InfraOps and MLOps, including SLAs, SLOs, alerting, performance/cost tracking and continuous optimization.
Integrate AI/ML systems into enterprise environments while ensuring interoperability, security, compliance, regulatory alignment and adherence to client standards.
Collaborate with clients, stakeholders, architects and engineering teams to align infrastructure decisions with business outcomes and translate requirements into actionable architecture standards.
Set technical direction for workstreams, mentor engineers, review designs/code and promote engineering best practices across the team.
Required Qualifications
Bachelor's degree in Computer Science, Computer Engineering, Information Technology or a related engineering field.
Minimum 4 years of experience coding, building, monitoring, troubleshooting, designing and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines or large-scale engineering solutions.
Strong understanding of AI/ML concepts and the computing infrastructure required to deploy, run and optimize production AI workloads.
Minimum 4 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell or equivalent engineering languages.
Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services or platform-native workflow tooling.
Strong problem-solving skills and ability to work in a fast-paced engineering or client delivery environment.
Excellent communication, collaboration and stakeholder alignment skills.
Minimum 4 years of experience in AI/ML infrastructure engineering or related roles on a hyperscaler or enterprise platform for deploying large-scale solutions.
Proven experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives.
Demonstrated experience evaluating and selecting AI technologies, frameworks, cloud services and architecture patterns.
Required Skills/ Experience
Strong hands-on experience with Snowflake warehouses, databases, schemas, secure data sharing/access controls, Snowpark, Cortex/AI capabilities, Streams/Tasks, Streamlit and cloud ecosystem integrations.
Experience architecting scalable data and feature pipelines, AI application integrations, model-enablement patterns, governance, query/warehouse optimization and secure data access.
Strong working knowledge of SQL, Python, dbt/Terraform, CI/CD, DataOps, MLOps/AI enablement patterns, observability and incident response practices.
Ability to optimize Snowflake AI infrastructure for performance, cost, scalability, security, reliability and compliance.
Experience producing architecture decision records, reference implementations, standards, runbooks and reusable platform patterns.
Good to Have Skills
Snowflake certifications such as SnowPro Advanced Architect, SnowPro Advanced Data Engineer or related AI/data platform credentials.
Industry experience in BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy or public sector environments where data/AI platforms must meet compliance, security, reliability and cost-control requirements.
Exposure to Snowpark, Cortex/AI features, vector search, retrieval pipelines, feature engineering, model enablement and AI application architecture.
Knowledge of Snowflake governance, secure data sharing, FinOps, infrastructure partner/vendor collaboration and production support operating models.
15 years full time education
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
Equal Employment Opportunity Statement
We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
Skills Required
- Minimum 12 years of experience
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology or related engineering field
- Minimum 4 years coding, building, monitoring, troubleshooting, designing and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines or large-scale engineering solutions
- Minimum 4 years proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell
- Strong hands-on experience with Snowflake warehouses, databases, schemas, secure data sharing/access controls, Snowpark, Cortex/AI capabilities, Streams/Tasks and cloud integrations
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, or managed/platform-native orchestration tooling
- Strong working knowledge of SQL, dbt, Terraform, CI/CD, DataOps, MLOps, observability and incident response practices
- Minimum 4 years of experience in AI/ML infrastructure engineering or related roles on a hyperscaler or enterprise platform
- Proven experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives
- Experience producing architecture decision records, reference implementations, standards, runbooks and reusable platform patterns
- Excellent communication, collaboration and stakeholder alignment skills
- 15 years full time education
- Snowflake certifications such as SnowPro Advanced Architect or SnowPro Advanced Data Engineer
- Industry experience in BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy or public sector (compliance/regulatory environments)
- Exposure to vector search, retrieval pipelines, feature engineering, model enablement and AI application architecture
Accenture Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Accenture and has not been reviewed or approved by Accenture.
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Healthcare Strength — Pay is considered competitive when paired with robust insurance options and other perks that compare well with large consulting and IT services peers. Multiple national medical plan options plus dental and vision are positioned as a core strength of the overall package.
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Retirement Support — Retirement support is positioned as a standout feature through a 401(k) dollar-for-dollar match up to a set percentage after eligibility. The package is reinforced by additional financial programs such as savings tools and related resources.
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Parental & Family Support — Parental and caregiving supports are presented as a meaningful benefit differentiator through substantial paid parental leave and multiple caregiver-oriented programs. Backup care and fertility/adoption/surrogacy navigation and reimbursements add breadth to family support beyond leave alone.
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The Company
What We Do
Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Interactive, Technology and Operations services—all powered by the world’s largest network of Advanced Technology and Intelligent Operations centers. Our 500,000+ people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities. Visit us at www.accenture.com.
