3 days ago
Coimbatore, IndiaSenior
Responsibilities
- Own the end-to-end architecture and design of Snowflake data and AI infrastructure, including warehouses, Snowpark workloads, secure data architecture, feature and data pipelines, model integration, and AI application enablement.
- Design and optimize Snowflake warehouses, tasks, streams, Snowpark services, Cortex/AI capabilities, Streamlit applications, and cloud integrations.
- Evaluate architecture alternatives across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost, and operational complexity.
- Lead architecture reviews, identify risks and bottlenecks, recommend remediation, and document decisions, trade-offs, assumptions, and dependencies.
- Define infrastructure roadmaps, capacity plans, scaling strategies, cost forecasts, and performance improvements.
- Design deployment automation and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish monitoring and observability practices across InfraOps and MLOps, including SLAs, SLOs, alerting, and performance and cost tracking.
- Integrate AI/ML systems into enterprise environments while ensuring interoperability, security, compliance, and regulatory alignment.
- Collaborate with clients, stakeholders, architects, and engineering teams to translate business requirements into architecture standards.
- Set technical direction, mentor engineers, review designs and code, and promote engineering best practices.
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; the posting also specifies at least 4 years in AI/ML infrastructure engineering or related large-scale engineering roles.
- At least 4 years of programming or scripting experience with languages such as Python, Java, C++, Bash, or PowerShell is required.
- Experience building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale engineering solutions is required.
- Strong understanding of AI/ML concepts and infrastructure for production AI workloads is required.
- Experience with data pipeline and workflow tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling is required.
- Hands-on experience with Snowflake warehouses, databases, schemas, secure data sharing and access controls, Snowpark, Cortex/AI capabilities, Streams/Tasks, Streamlit, and cloud integrations is required.
- Experience with scalable data and feature pipelines, AI application integrations, model-enablement patterns, governance, query and warehouse optimization, and secure data access is required.
- Working knowledge of SQL, Python, dbt, Terraform, CI/CD, DataOps, MLOps, observability, and incident response is required.
- Experience leading AI projects or engineering workstreams, evaluating AI technologies and architecture patterns, and managing priorities across multiple initiatives is required.
- Snowflake certifications such as SnowPro Advanced Architect or SnowPro Advanced Data Engineer, related AI/data platform credentials, and regulated-industry experience are preferred.
- Exposure to vector search, retrieval pipelines, feature engineering, model enablement, Snowflake governance, FinOps, vendor collaboration, and production support operating models is preferred.
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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.