20 days ago
Gurgaon, 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 tune 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 assessments, identify risks and optimization opportunities, and document architecture decisions, standards, runbooks, and reusable platform patterns.
- Define infrastructure roadmaps, capacity planning, scaling strategies, cost forecasts, and performance improvements.
- Design deployment, automation, and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish AI monitoring and observability across InfraOps and MLOps, including SLAs, SLOs, alerting, performance tracking, cost tracking, and incident response.
- Integrate AI/ML systems into enterprise environments while maintaining 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 lead AI projects or engineering workstreams.
Requirements
- Minimum 7.5 years of experience is required.
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field 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 programming or scripting experience with languages such as Python, Java, C++, Bash, or PowerShell.
- At least 4 years of AI/ML infrastructure engineering or related experience on a hyperscaler or enterprise platform for deploying large-scale solutions.
- Strong understanding of AI/ML concepts and infrastructure for production AI workloads.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- 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, Python, dbt, Terraform, CI/CD, DataOps, MLOps, AI enablement patterns, observability, and incident response.
- Experience producing architecture decision records, reference implementations, standards, runbooks, and reusable platform patterns.
- Proven experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives.
- Experience evaluating and selecting AI technologies, frameworks, cloud services, and architecture patterns.
- Strong problem-solving, communication, collaboration, and stakeholder-alignment skills.
- Preferred qualifications include Snowflake certifications such as SnowPro Advanced Architect or SnowPro Advanced Data Engineer, related AI/data platform credentials, regulated-industry experience, vector search and retrieval pipeline exposure, feature engineering, model enablement, Snowflake governance, FinOps, and infrastructure partner/vendor collaboration.
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.