Agentic AI Engineer

India·Bangaloremid

OtherAgentic Ai Engineer

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Key Responsibilities

ML System Development: Design, develop, and maintain scalable and efficient machine learning systems, including writing ML services and APIs.

Technical Tools

OtherAgentic Ai Engineer

~1 min read

→ML System Development: Design, develop, and maintain scalable and efficient machine learning systems, including writing ML services and APIs.
→Model Deployment: Implement and manage the deployment of machine learning models, including transformer based LLMs, into production environments, ensuring reliability and scalability.
→Infrastructure Management: Collaborate with infrastructure teams to optimize and manage the underlying systems supporting machine learning workflows.
→Data Pipeline Creation: Create robust and efficient data pipelines for collecting, processing, and preparing datasets for machine learning models.
→Collaboration: Work closely with data scientists, researchers, and cross-functional teams to integrate ML solutions into existing software infrastructure.
→Performance Optimization: Continuously optimize and improve the performance of machine learning algorithms and systems.
→Documentation: Develop and maintain documentation for machine learning systems, APIs, and data pipelines to ensure clarity and ease of use for team members.

3+ years of experience including working on designing multi-component systems
Strong grasp of one high-level language like Python.
General awareness of SQL and database design concepts
Solid understanding of testing fundamentals
Strong communication skills
should have prior experience in managing and executing technology products.
Decent understanding of various Gen AI based ML approaches
Experience in building agentic architectures using langgraph or similar libraries

~1 min read

Prior experience working with high-volume, always-available web-applications
Experience working with cloud
Knowledge of cloud platforms such as AWS, GCP, or Azure.
Experience with deploying small and big open source LLMs in production environments using containerization tools like Docker
Experience in Distributed systems.
Experience working with Start-up is a plus point.