24 days ago
Kolkāta, IndiaSenior
Responsibilities
- Design and implement Snowflake-based AI/ML infrastructure for secure data access, feature preparation, model enablement, AI application integration, and production analytics.
- Write, review, and debug SQL, Python, scripts, and infrastructure-as-code for automation, monitoring, and deployment tooling.
- Configure and manage Snowflake warehouses, databases, schemas, secure access controls, Snowpark workloads, Streamlit applications, pipelines, and integrations.
- Support deployment automation and infrastructure operations using Git, Terraform, dbt, containers, Kubernetes, and workflow orchestration tools.
- Deploy, monitor, troubleshoot, and optimize data pipelines, AI application integrations, warehouse utilization, query performance, and integration health.
- Apply security, governance, reliability, scalability, and cost-efficiency practices to enterprise AI platforms.
- Collaborate with data scientists, ML engineers, data engineers, platform engineers, and architects to integrate Snowflake-enabled AI solutions.
- Document reusable architecture patterns, configuration standards, and operational runbooks.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- At least 5 years of professional experience is stated as required; the qualifications also specify at least 2 years working with AI/ML infrastructure, data platforms, model deployment pipelines, or cloud/platform engineering solutions.
- At least 2 years of proficiency with programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Hands-on experience with Snowflake warehouses, databases, schemas, Snowpark, Python/SQL workloads, secure data sharing and access controls, and cloud storage integrations.
- Experience designing or operating scalable data pipelines, feature preparation workloads, AI application integrations, production analytics, or ML enablement workloads.
- Experience with infrastructure-as-code, containers, Kubernetes, workflow orchestration, operational monitoring, and deployment automation.
- Understanding of AI/ML concepts, compute, storage, networking, security, deployment foundations, MLOps, feature engineering, model input/output management, monitoring, and governance.
- Ability to optimize warehouses, queries, pipelines, and integrations for performance, reliability, scalability, cost, and security.
- Preferred qualifications include Snowflake certifications such as SnowPro Core, SnowPro Advanced Architect, or SnowPro Data Engineer.
- Preferred experience includes Snowpark, Cortex/AI features, vector search, retrieval pipelines, data governance, data-sharing controls, FinOps, incident management, production support, and regulated-industry use cases.
Categories
Data 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.