AI Infrastructure Architect
Coimbatore
Posted 14h ago
Job Location
Coimbatore
Tech Stack
Remote Work Policy
On-site
Categories
AI Infrastructure Engineer
About the job
As a Technical Architect in AI Infrastructure Architecture, you will act as a senior technical authority for Snowflake-based AI/ML and data platform infrastructure. You will shape the technical vision, reference architecture, standards, and implementation strategy for enterprise AI systems. This role involves evaluating complex choices across warehouses, secure data architecture, Snowpark, AI application enablement, feature/data pipelines, governance, observability, security, and cost optimization. You will guide senior architects to deliver resilient, scalable, and production-ready AI infrastructure, bringing industry experience across enterprise AI adoption, compliance, reliability, FinOps, and platform modernization to translate AI infrastructure trade-offs into measurable business value.
Responsibilities
- Set the overarching Snowflake AI infrastructure vision, strategy, and reference architecture for enterprise AI/ML and data platform systems.
- Own complex architectural decisions across Snowflake components and cloud integrations, rationalizing options against client standards and business objectives.
- Architect and prototype cost-optimized AI-enabling data/feature pipelines, Snowpark workloads, model integration patterns, and AI application environments.
- Define architecture standards, reusable infrastructure-as-code patterns, CI/CD approaches, data/model enablement patterns, monitoring strategy, SLAs/SLOs, and cost/performance governance for production AI systems.
- Lead architecture assessments and design reviews, validating findings through hands-on implementation, profiling, performance tuning, and troubleshooting.
- Evaluate emerging Snowflake AI, vector search, governance, interoperability, and data application capabilities, recommending their integration into enterprise solutions.
- Provide executive and client-level technical advisory, translating platform trade-offs into clear, defensible recommendations connected to business outcomes.
- Mentor architects and Technical Architects, build community best practices, and represent the practice in technical forums.
Requirements
- Bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology, or a related field.
- Minimum 6 years of experience coding, building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale solutions.
- Strong understanding of AI/ML concepts and the compute, infrastructure, orchestration, and deployment foundations required for production AI systems.
- Minimum 6 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell, or equivalent.
- Experience with data pipeline and workflow management tools like Apache Airflow, Kubeflow, or managed orchestration services.
- Proven experience leading AI infrastructure projects and teams, including technical direction, design reviews, delivery governance, and stakeholder alignment.
- Strong project management, communication, problem-solving, and cross-functional collaboration skills.
- Demonstrated experience evaluating and selecting AI technologies, frameworks, reference architectures, and platform services for production solutions.
- Expert-level hands-on architecture experience with Snowflake warehouses, databases, schemas, secure data sharing/access controls, Snowpark, Cortex/AI capabilities, Streams/Tasks, Streamlit, and cloud ecosystem integrations.
- Deep knowledge of scalable data architecture, AI feature/data pipelines, model integration patterns, governance, query/warehouse optimization, observability, and resilience.
- Strong experience with SQL, Python, dbt/Terraform, CI/CD, security guardrails, monitoring, and platform cost optimization.
- Ability to evaluate multiple Snowflake architecture options and produce standards, patterns, decision records, benchmarks, and executive-ready recommendations.
- Experience applying MLOps/DataOps/InfraOps practices for production data/AI pipelines, model enablement, monitoring, incident response, and rollback strategies.