AI Infrastructure Architect
Remote • Kolkata
Posted 14h ago
About the job
As a Senior Engineer in AI Infrastructure Architecture, you will be responsible for the end-to-end architecture and engineering of optimized data and AI infrastructure for production machine learning and AI-enabled applications. This includes designing scalable warehouses, AI-ready data/feature pipelines, model-enablement patterns, and automation controls that meet client standards for security, compliance, and cost-efficiency. You will leverage your industry experience in enterprise AI adoption, platform modernization, and production reliability to mentor engineers and translate business requirements into robust AI infrastructure solutions.
Responsibilities
- Own the end-to-end architecture and design of optimized data and AI infrastructure, including warehouses, secure data architecture, AI-ready pipelines, and model integration.
- Design and tune scalable warehouses, Snowpark services, AI capabilities, and cloud integrations, focusing on compute sizing, query optimization, governance, and data access.
- Serve as an expert on AI infrastructure, applying deep knowledge of data cloud capabilities, AI application patterns, governance, security, and cost management.
- Develop and evaluate architecture alternatives, considering trade-offs across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost, and operational complexity.
- Lead architecture assessments and reviews, identifying gaps, risks, and optimization opportunities, and recommending remediation actions.
- Drive architecture decision-making by documenting rationale, trade-offs, assumptions, and dependencies.
- Define and maintain AI infrastructure roadmap inputs, capacity planning models, scaling strategies, cost forecasts, and performance improvement opportunities.
- Design deployment, automation, and CI/CD strategies for reliable and scalable releases of AI systems, models, and platform components.
- Establish AI monitoring and observability practices, including SLAs, SLOs, alerting, and performance/cost tracking.
- Integrate AI/ML systems into enterprise environments, ensuring interoperability, security, compliance, and adherence to client standards.
- Collaborate with clients, stakeholders, and engineering teams to align infrastructure decisions with business outcomes.
- Set technical direction, mentor engineers, review designs/code, and promote engineering best practices.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- Minimum 4 years of experience in coding, building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale engineering solutions.
- Strong understanding of AI/ML concepts and the computing infrastructure required for production AI workloads.
- Minimum 4 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.
- Strong problem-solving skills and ability to work in a fast-paced engineering or client delivery environment.
- Excellent communication, collaboration, and stakeholder alignment skills.
- Minimum 4 years of experience in AI/ML infrastructure engineering or related roles on a hyperscaler or enterprise platform.
- Proven experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives.
- Demonstrated experience evaluating and selecting AI technologies, frameworks, cloud services, and architecture patterns.
- Strong hands-on experience with Snowflake warehouses, databases, schemas, secure data sharing/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/warehouse optimization, and secure data access.
- Strong working knowledge of SQL, Python, dbt/Terraform, CI/CD, DataOps, MLOps/AI enablement patterns, observability, and incident response practices.
- Ability to optimize Snowflake AI infrastructure for performance, cost, scalability, security, reliability, and compliance.
- Experience producing architecture decision records, reference implementations, standards, runbooks, and reusable platform patterns.