Description

Key Skills: MLOps, Deployment, Google Cloud Platform, Pipeline, Data architecture, LLM, Governance, Vector Database Integration, Data modeling, ETL, Agentic AI

Roles and Responsibilities:

• Design enterprise AI/ML architectures that connect ML pipelines to production AI deployment workflows.
• Define and implement MLOps practices for training, evaluation, deployment, monitoring, and lifecycle management.
• Architect scalable AI systems that meet performance, reliability, and operational requirements in cloud environments.
• Lead technical design for AI-ready data architecture, including data modeling and governance alignment.
• Collaborate with engineering teams to operationalize AI infrastructure components and support GenAI/LLM use cases.

Skills Required:

• 8-15 years of experience designing enterprise AI/ML architectures and production AI systems.
• ML pipelines for end-to-end model development and production workflows.
• AI deployment patterns for reliable release, rollout, and operationalization of AI services.
• Cloud platforms experience to design and run scalable AI systems.
• MLOps expertise to standardize model lifecycle, automation, and production operations.

Good to Have:

• Data architecture and AI data pipelines, including enterprise data platforms and ETL/ELT.
• Data modeling, governance, and AI-ready data architecture for enterprise adoption.
• LLMs, multi-agent systems, AI infrastructure, and vector databases for GenAI solutions.
• Cloud data ecosystems, cloud AI ecosystems, and experience with enterprise AI transformation initiatives.

Education : Bachelor's in Computer Science or related field; Master's in Computer Science preferred

Key Skills

Education

Any Graduate