4 days ago
Mumbai, IndiaSenior
Responsibilities
- Own the end-to-end architecture and design of Snowflake data and AI infrastructure, including warehouses, Snowpark workloads, secure data architecture, pipelines, model integration, and AI application enablement.
- Design and optimize Snowflake warehouses, tasks, streams, Snowpark services, Cortex/AI capabilities, Streamlit applications, and cloud integrations for performance, cost, scalability, governance, and secure access.
- Evaluate architecture alternatives across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost, and operational complexity.
- Lead architecture assessments and reviews, identify risks and optimization opportunities, and document decisions, trade-offs, standards, runbooks, and reusable platform patterns.
- Define roadmap inputs, capacity planning models, scaling strategies, cost forecasts, deployment automation, CI/CD strategies, monitoring, SLAs, SLOs, and incident response practices.
- Integrate AI/ML systems into enterprise environments while ensuring interoperability, security, regulatory alignment, reliability, and compliance.
- Collaborate with clients, stakeholders, architects, and engineering teams to translate business requirements into actionable architecture standards.
- Set technical direction for workstreams, mentor engineers, review designs and code, and promote engineering best practices.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field, with 15 years of full-time education.
- Minimum 7.5 years of experience is required.
- At least 4 years of experience building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale engineering solutions.
- At least 4 years of proficiency with programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Experience with Snowflake warehouses, databases, schemas, secure data sharing and 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 and warehouse optimization, and secure data access.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Strong working knowledge of SQL, Python, dbt, Terraform, observability, incident response, DataOps, and MLOps/AI enablement patterns.
- 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, evaluating AI technologies and architecture patterns, and managing priorities across multiple initiatives.
- Preferred qualifications include Snowflake certifications, experience in regulated industries, and exposure to vector search, retrieval pipelines, feature engineering, model enablement, FinOps, and infrastructure vendor collaboration.
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About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.