Project Role : Data Platform Architect
Project Role Description : Architects the data platform blueprint and implements the design, encompassing the relevant data platform components. Collaborates with the Integration Architects and Data Architects to ensure cohesive integration between systems and data models.
Must have skills : Snowflake Data Warehouse
Good to have skills : Machine Learning Operations
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
As a hands-on Engineer in AI Infrastructure Architecture, you will design, build, automate, monitor and optimize Snowflake-based AI/ML infrastructure for secure data access, feature preparation, model enablement, AI application integration and production analytics workloads. you will work on moderately complex platform components under guidance from senior architects and engineers, contributing to compute optimization, deployment automation, observability, governance, security and operational reliability for AI-driven business solutions.
Key Responsibilities
Write, review and debug SQL, Python, scripts and infrastructure-as-code for Snowflake AI/ML infrastructure, automation, monitoring and deployment tooling.
Configure and manage Snowflake warehouses, databases, schemas, secure access patterns, Snowpark workloads, Streamlit apps, model-related data pipelines and integrations with cloud storage and orchestration tools.
Support deployment automation and CI/CD pipelines for Snowflake-based AI solutions using tools such as Git, Terraform, dbt, Python, containers and workflow orchestration tooling where applicable.
Deploy and operate data/feature pipelines, AI application integrations and model-enablement components while applying reliability, security, cost-efficiency and scalability practices.
Monitor warehouse utilization, query performance, pipelines and integration health troubleshoot issues across compute, storage, access control, data movement and application layers.
Collaborate with data scientists, ML engineers, data engineers, platform engineers and architects to integrate Snowflake-enabled AI solutions into enterprise systems while meeting compliance and operational requirements.
Document reusable patterns, configuration standards and runbooks for Snowflake-based AI infrastructure.
Required Qualifications
Bachelor's degree in Computer Science, Computer Engineering, Information Technology or a related engineering field.
Minimum 2 years of experience coding, building, monitoring or troubleshooting AI/ML infrastructure, data platforms, model deployment pipelines or cloud/platform engineering solutions.
Strong understanding of AI/ML concepts and the compute, storage, networking, security and deployment foundations required to run AI workloads.
Minimum 2 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash or PowerShell.
Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration and operational monitoring tools.
Strong problem-solving ability, communication skills and collaboration mindset in a fast-paced engineering environment.
Required Skills/ Experience
Hands-on experience with Snowflake warehouses, databases, schemas, secure data sharing/access controls, Snowpark, Python/SQL workloads and cloud storage integrations.
Experience designing or operating scalable data pipelines, feature preparation workloads, AI application integrations and production analytics or ML enablement workloads.
Working knowledge of SQL, Python, dbt/Terraform, CI/CD pipelines, data observability and cost/performance optimization practices.
Ability to optimize warehouses, queries, data pipelines and integrations for performance, reliability, scalability, cost and security.
Understanding of MLOps/data platform patterns including feature engineering, model input/output management, monitoring and governance.
Good to Have Skills
Snowflake certification such as SnowPro Core, SnowPro Advanced Architect, SnowPro Data Engineer or related platform credentials.
Exposure to industry use cases in BFSI, healthcare, retail/e-commerce, telecom, manufacturing or public sector where data/AI platforms must meet compliance, reliability and data-governance expectations.
Familiarity with Snowpark, Cortex/AI features, vector search, retrieval pipelines, feature engineering and model-enablement patterns.
Knowledge of data governance, data sharing controls, FinOps practices, incident management and production support processes for enterprise AI platforms.
15 years full time education
