24 days ago
Coimbatore, IndiaSenior
Responsibilities
- Architect and implement scalable Snowflake data and AI infrastructure, including warehouses, Snowpark workloads, secure data architectures, pipelines, model integration, and AI application enablement.
- Design and optimize Snowflake warehouses, databases, schemas, Streams, Tasks, Snowpark services, Cortex/AI capabilities, Streamlit applications, and cloud integrations.
- Lead architecture assessments, reviews, decision records, roadmap planning, capacity modeling, cost forecasting, and performance optimization.
- Define deployment, automation, CI/CD, monitoring, observability, alerting, incident response, and operational practices for AI systems, models, pipelines, and platform components.
- Ensure AI/ML platform interoperability, security, governance, compliance, reliability, and adherence to client standards and SLAs.
- Collaborate with clients, stakeholders, architects, and engineering teams to translate business requirements into architecture standards.
- Set technical direction, lead engineering workstreams, mentor engineers, and review designs and code.
Requirements
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field is required.
- At least 7.5 years of experience is required for the project role.
- At least 4 years of experience with AI/ML infrastructure engineering or related work involving 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.
- Hands-on 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.
- Working knowledge of SQL, dbt, Terraform, DataOps, MLOps/AI enablement patterns, observability, and incident response practices.
- Experience with data pipeline and workflow tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Experience leading AI projects or engineering workstreams and evaluating AI technologies, frameworks, cloud services, and architecture patterns.
- Preferred qualifications include Snowflake certifications such as SnowPro Advanced Architect or SnowPro Advanced Data Engineer, regulated-industry experience, and knowledge of Snowflake governance, FinOps, vector search, retrieval pipelines, feature engineering, and production support models.
Categories
Data EngineeringML Engineering
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.