24 days ago
Pune, IndiaStaff+
Responsibilities
- Define Snowflake AI infrastructure vision, strategy, reference architectures, standards, and implementation approaches for enterprise AI/ML and data platforms.
- Own architectural decisions involving Snowflake warehouses, databases, schemas, secure data sharing and access controls, Snowpark, Streams/Tasks, Cortex/AI capabilities, Streamlit, and cloud integrations.
- Architect and prototype cost-optimized AI-enabling data and feature pipelines, Snowpark workloads, model integration patterns, and AI application environments.
- Define infrastructure-as-code patterns, CI/CD approaches, monitoring strategies, SLAs/SLOs, and cost and performance governance for production AI systems.
- Lead architecture assessments and design reviews, including hands-on implementation, profiling, performance tuning, and troubleshooting.
- Evaluate Snowflake AI, vector search, governance, interoperability, and data application capabilities for enterprise solutions.
- Provide executive and client technical advisory and translate platform trade-offs into business recommendations.
- Lead AI infrastructure projects and teams, mentor architects, establish community practices, and represent the practice in technical forums.
Requirements
- A bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology, or a related technical field is required.
- The posting states a minimum of 18 years of experience, and also specifies at least 6 years building, operating, and architecting AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale technical architecture solutions.
- At least 6 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell, or equivalent languages is required.
- Expert 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 is required.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling is required.
- Strong knowledge of AI/ML concepts, scalable data architecture, feature and data pipelines, model integration, governance, observability, resilience, SQL, dbt/Terraform, security guardrails, monitoring, and platform cost optimization is required.
- Experience leading AI infrastructure projects and teams, including technical direction, design reviews, delivery governance, and stakeholder alignment, is required.
- Experience applying MLOps, DataOps, or InfraOps practices to production pipelines, model enablement, monitoring, incident response, and rollback strategies is required.
- Preferred qualifications include Snowflake certifications, industry experience in regulated or enterprise sectors, exposure to vector search and retrieval pipelines, and experience with data governance, enterprise architecture roadmaps, vendor or partner management, FinOps, and production support models.
Categories
Data EngineeringDevOps
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.