Architects and engineers scalable Snowflake-based AI and data infrastructure for production applications. Designs warehouses, Snowpark workloads, AI-ready pipelines, model integrations, governance, CI/CD, observability, and cost optimization. Evaluates infrastructure technologies, leads architecture reviews, defines roadmaps and standards, ensures security and compliance, and mentors engineering teams while aligning technical solutions with client requirements and business outcomes.
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 7.5 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 7.5 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.
Accenture Airoli, Mahārāshtra, IND Office
Airoli, India
Accenture Mumbai, Maharashtra, IND Office
Ganpatrao Kadam Marg, off Senapati Bapat Marg, Lower Parel West, Lower Parel, Mumbai, Maharashtra, India, 400013
Similar Jobs
Cloud • Information Technology • Security • Software • Cybersecurity
Leads cybersecurity data-source onboarding and integration for Zscaler’s security platform. Builds and automates Python/API-based data transformations, maps and normalizes security data, manages pipeline lifecycle activities, troubleshoots data quality, and identifies security gaps. Partners with cybersecurity SMEs and cross-functional teams to create implementation plans, communicate technical findings, and provide product improvement feedback. The role requires expertise in security platforms, diverse security-tool integrations, data modeling, SQL, and Python.
Top Skills:
Ai/MlAPIsCloud LogsCmdbCnappCspmEdrPythonSIEMSQLUnified Vulnerability Management
Healthtech • Logistics • Pharmaceutical
Leads enterprise architecture strategy, road maps, standards, and technology alignment with business objectives. Evaluates current-state architectures, guides ERP/CRM and cross-functional solutions, assesses build-versus-buy options, supports innovation and transformation, and advises on mergers and acquisitions. Collaborates with business and technology stakeholders, oversees architectural governance, mentors architects, and may manage architecture teams.
Top Skills:
Cloud ArchitectureCots SolutionsCRMEnterprise ArchitectureErpSystem Integration
Financial Services
Lead design, build, deployment, and support of IBM Planning Analytics/TM1 solutions on AWS. Architect TM1 models, develop PAW/PAfE artifacts, manage TI processes and integrations, establish governance and SDLC controls, lead a TM1 team, drive AI-assisted engineering adoption, and partner with business stakeholders to optimize planning processes and ensure compliance and resiliency.
Top Skills:
ArchitectAWSCi/CdCognos AnalyticsContainerizationDatabricksIbm Cloud For DataIbm Planning AnalyticsPerspectivesPlanning Analytics For Excel (Pafe)Planning Analytics On CloudPlanning Analytics Workspace (Paw)PythonTm1Turbointegrator (Ti)
What you need to know about the Mumbai Tech Scene
From haggling for the best price at Chor Bazaar to the bustle of Crawford Market, the energy of Mumbai's traditional markets is a key part of the city's charm. And while these markets will always have their place, the city also boasts a thriving e-commerce scene, ranking among the largest in the region. Driven by online sales in everything from snacks to licensed sports merchandise to children's apparel, the local industry is worth billions, with companies actively recruiting to meet the demands of continued growth.
