AI Infrastructure Architect

Role summary: Senior AI Infrastructure Architect responsible for architecting and building optimized AI infrastructure and hardware solutions (with a strong focus on Snowflake and cloud integrations) to support production machine learning and AI-enabled applications. The role covers end-to-end architecture, performance/cost optimization, security/compliance alignment, operationalization and mentoring of engineering teams. Key responsibilities: - Own end-to-end design and architecture of Snowflake data and AI infrastructure: warehouses, Snowpark workloads, secure data architecture, AI-ready feature/data pipelines, model integration and enablement patterns. - Design and tune scalable Snowflake warehouses, Snowpark services, Streams/Tasks, Cortex/AI features, Streamlit apps and cloud integrations including compute sizing, query optimization, governance, access controls and high-throughput data access. - Evaluate architecture alternatives and drive decision-making with documented rationale, trade-offs, assumptions and dependencies. Lead architecture assessments, identify gaps/risks/bottlenecks and recommend remediation. - Define AI infrastructure roadmaps, capacity planning, scaling strategies, cost forecasts (FinOps) and performance improvement opportunities. - Design deployment, automation and CI/CD strategies for repeatable, reliable releases of AI systems, models, data pipelines and platform components. - Establish monitoring and observability practices across InfraOps and MLOps (SLAs, SLOs, alerting, performance/cost tracking) and integrate AI/ML systems into enterprise environments while ensuring interoperability, security and regulatory compliance. - Mentor engineers, set technical direction, review designs/code and produce architecture decision records, reference implementations, standards, runbooks and reusable platform patterns. Required qualifications and experience: - 15 years full-time education; Bachelor’s degree in Computer Science, Computer Engineering, Information Technology or related engineering field. - Minimum 12 years of overall experience (posting indicates senior level). - Minimum 4 years coding/building/operating AI/ML infrastructure, cloud platforms, data platforms or model deployment pipelines and minimum 4 years proficiency in programming/scripting such as Python, Java, C++, Bash or PowerShell. - Strong understanding of AI/ML concepts and computing infrastructure to deploy, run and optimize production AI workloads. - Experience with data pipeline and workflow management tools (Apache Airflow, Kubeflow or managed orchestration services) and production reliability practices. - Proven experience leading AI projects or engineering workstreams and evaluating/selecting AI technologies, frameworks and cloud services. Good-to-have / preferred: - Snowflake certifications (e.g., SnowPro Advanced Architect, SnowPro Advanced Data Engineer) and industry experience in regulated sectors (BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy, public sector). - Exposure to Snowpark, Cortex/AI features, vector search, retrieval pipelines, feature engineering and AI application architecture. - Knowledge of Snowflake governance, secure data sharing, FinOps, vendor/infrastructure partner collaboration and production support operating models. Tools, techniques and environment: - Heavy emphasis on Snowflake Data Cloud (warehouses, Snowpark, Streams/Tasks, Cortex/AI), Streamlit, SQL, Python and cloud ecosystem integrations (including Microsoft Azure Data Services). - Use of dbt/Terraform, CI/CD, DataOps, MLOps patterns, observability and incident response practices; workflow tools such as Airflow or Kubeflow. Location and other information: - Location: Chennai (role listed on Accenture India careers site). - Employment type: Full-time. - No salary provided in posting.