New opportunity

AI Infrastructure Architect

Accenture · Kolkata, India

About this role

As an AI Infrastructure Architect at Accenture, you will own 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, and operational controls that align with client standards, security, and cost-efficiency. Key Responsibilities: Own the architecture of Snowflake data and AI infrastructure, including compute sizing, query optimization, and governance. Design and tune scalable Snowflake services, Cortex/AI capabilities, and cloud integrations. Lead architecture assessments to identify risks and optimization opportunities. Define AI infrastructure roadmaps, capacity planning, and cost forecasts. Establish CI/CD strategies and MLOps/InfraOps monitoring practices. Mentor engineers and set technical direction for workstreams. Required Qualifications: Bachelor's degree in Computer Science, Engineering, or IT. Minimum 12 years of total experience, with at least 4 years specifically in AI/ML infrastructure engineering, cloud platforms, or large-scale data platform operations. Proficiency in programming languages such as Python, Java, C++, or scripting languages. Experience with workflow management tools like Apache Airflow or Kubeflow. Strong problem-solving and stakeholder alignment skills. Good to have: Snowflake certifications (SnowPro Advanced Architect/Data Engineer), industry experience in regulated sectors (BFSI, healthcare, etc.), and knowledge of vector search, feature engineering, and FinOps.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI Agents & Workflow IntegrationSnowflake Data WarehouseSnowparkPythonJavaC++BashPowerShellSQLdbtTerraformCI/CDDataOpsMLOpsApache AirflowKubeflowCortex/AIStreamlit

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Snowflake AI Infrastructure Optimization
The role requires deep expertise in architecting and tuning Snowflake environments, specifically focusing on compute sizing, query optimization, and the integration of Snowpark and Cortex/AI capabilities to ensure high-performance production AI workloads.
End-to-End MLOps and CI/CD Governance
You must establish robust automation, CI/CD pipelines, and monitoring practices for AI systems. This includes defining standards for model deployment, data pipeline reliability, and observability to ensure production systems meet client SLAs and security compliance.
Strategic Architecture and Stakeholder Alignment
As an architect, you are responsible for translating complex business requirements into actionable infrastructure roadmaps. This involves documenting architecture decision records, managing capacity planning, and aligning technical solutions with FinOps and regulatory standards.

Likely challenges to think through

Balancing Performance with Cost-Efficiency
A core challenge is optimizing AI infrastructure for both high throughput and strict cost-control. Prepare by demonstrating how you have utilized FinOps principles to manage cloud spend without compromising the performance or scalability of large-scale data and AI pipelines.
Navigating Regulatory and Compliance Constraints
Working in regulated sectors like BFSI or healthcare requires strict adherence to security and data governance. Prepare by discussing how you design secure data architectures and access controls that satisfy rigorous compliance audits while maintaining operational agility.

Practice your answers

Technical practice

Optimizing Snowflake Compute for AI Workloads

What to coverExplain your approach to sizing and tuning Snowflake warehouses for high-throughput AI and Snowpark workloads. Discuss how you balance performance requirements with cost-efficiency, specifically addressing query optimization and warehouse scaling strategies.

Designing AI-Ready Data Pipelines

What to coverDescribe the architecture of a scalable feature pipeline within the Snowflake ecosystem. Focus on how you ensure data quality, security, and accessibility for model training and inference while integrating with tools like dbt or Apache Airflow.

Implementing MLOps and CI/CD for AI Systems

What to coverHow do you establish reliable, repeatable deployment strategies for AI models and infrastructure components? Detail your experience with CI/CD pipelines and how you incorporate automated testing and monitoring to ensure production stability.

Snowflake Cortex and AI Integration

What to coverDiscuss your experience integrating Snowflake Cortex or other AI capabilities into enterprise applications. Explain how you handle model enablement patterns and ensure these integrations align with security and governance standards.

Governance and Security in AI Infrastructure

What to coverHow do you design secure data access controls and governance frameworks for AI platforms, especially in regulated sectors like BFSI or healthcare? Address how you manage secure data sharing while maintaining compliance.

FinOps and Cost Forecasting

What to coverExplain your methodology for capacity planning and cost forecasting for large-scale AI infrastructure. Describe how you implement FinOps practices to track performance and cost metrics to drive continuous optimization.

Observability and Incident Response

What to coverWhat are the key components of an effective observability strategy for MLOps and InfraOps? Explain how you define SLAs and SLOs and how you structure incident response for AI-enabled production applications.

Architecture Decision Records (ADRs)

What to coverWhy is it critical to document architecture rationale, trade-offs, and dependencies in large-scale AI projects? Describe your process for creating transparent, defensible architecture decision records that align with business outcomes.

Behavioural practice

Leading Architecture Assessments

What to coverDescribe a situation where you led an architecture assessment for an existing environment. How did you identify bottlenecks or risks, and what was your process for recommending and implementing remediation actions?

Stakeholder Alignment and Technical Direction

What to coverProvide an example of how you have translated complex business requirements into actionable infrastructure standards. How did you ensure alignment across different engineering teams and stakeholders?

Mentoring and Engineering Best Practices

What to coverTell us about a time you mentored engineers or set the technical direction for a workstream. How did you promote engineering best practices and ensure the team maintained high standards for code and design?

Navigating Trade-offs in Technology Selection

What to coverDescribe a project where you had to evaluate and select specific AI technologies or frameworks. How did you weigh the trade-offs across compute, networking, and operational complexity to make a final decision?

Sharpen your knowledge

Topics to prepare

Snowflake AI Infrastructure Architecture
Focus on the end-to-end design of Snowflake-based AI ecosystems. Exercise: Draft an architecture decision record (ADR) comparing the trade-offs between using Snowpark for feature engineering versus external compute, specifically addressing cost, latency, and data governance requirements for a regulated industry.
FinOps and Capacity Planning for AI
Understand how to forecast and manage costs for large-scale AI workloads. Exercise: Create a capacity planning model for a hypothetical LLM-enabled application, detailing how you would implement automated scaling and cost-tracking alerts within a Snowflake environment to ensure adherence to budget constraints.
MLOps and Infrastructure Observability
Master the integration of CI/CD and monitoring for AI systems. Exercise: Design a monitoring strategy for a production model pipeline that includes specific SLOs for latency and data drift, and define an incident response workflow for when these metrics deviate from established thresholds.

Skills to revise

Snowpark and Cortex/AI Implementation
Develop hands-on proficiency in building scalable data pipelines and AI-ready features using Snowpark. Exercise: Build a sample pipeline that utilizes Snowpark to perform feature engineering on a dataset and integrates a Cortex/AI function for model inference, ensuring secure data access controls are applied.
Infrastructure as Code (Terraform) for Data Platforms
Automate the deployment of data infrastructure. Exercise: Write a Terraform script to provision a scalable Snowflake warehouse and associated security roles, ensuring the configuration follows the principle of least privilege and is repeatable for different environments.
Workflow Orchestration with Apache Airflow
Manage complex dependencies in AI/ML pipelines. Exercise: Construct a Directed Acyclic Graph (DAG) in Airflow that orchestrates a multi-step process: data ingestion, feature transformation via dbt, and model deployment, including error handling and retry logic for production reliability.

Your preparation checklist

Develop a Snowflake AI Infrastructure Case Study
Prepare a technical walkthrough of a past project where you designed a scalable Snowflake architecture. Focus on how you managed compute sizing, Snowpark workloads, and cost-efficiency (FinOps) for production AI/ML pipelines.
Create an Architecture Decision Record (ADR) Template
Draft a sample ADR that demonstrates your process for evaluating trade-offs in AI infrastructure, such as choosing between different orchestration tools or model serving patterns, ensuring your rationale is transparent and defensible.
Map MLOps and CI/CD Integration Strategies
Outline a CI/CD pipeline design for deploying AI models into production on Snowflake. Include how you integrate monitoring, observability, and automated testing to ensure reliability and compliance in regulated environments.
Prepare Evidence of Governance and Security Implementation
Compile examples of how you have implemented secure data access, governance, and compliance controls within a large-scale data platform, specifically addressing the requirements of sectors like BFSI or healthcare.
Formulate Strategic Questions for Stakeholders
Prepare questions regarding the current maturity of their AI infrastructure, how they balance performance versus cost in their existing Snowflake environment, and the specific regulatory challenges they face in their current AI initiatives.

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