Staff/Principal Agentic Solutions Developer - Data scientist with AWS
Micron · Hyderabad, India
About this role
Micron is hiring a staff-level Machine Learning Engineer based in Hyderabad, India. The posting calls out experience with LLMs, Testing, AI Agents.
- Role
- Machine Learning Engineer
- Function
- machine learning
- Level
- staff
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Hyderabad, India
- Posted
- May 20, 2026
More roles at Micron
Job description
from Micron careersOur vision is to transform how the world uses information to enrich life for all.
Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.
Micron’s Data Science experts develop advanced AI and data-driven solutions to complex challenges in semiconductor process and fab technology development, product design, product engineering, yield analysis, and systems engineering. The team plays a key role in enabling intelligent automation and decision-making across Micron’s memory and storage portfolio.
Responsibilities
· Drive the creation and implementation of intelligent, ML-powered solutions that improve semiconductor composition, product development, verification and validation, and manufacturing workflows. Use large-scale, unstructured data to develop robust, domain-specific AI systems from problem definition to production.
· Architect and implement agentic AI systems that integrate with Advanced Modeling tools, EDA tools, design environments, and product/manufacturing test platforms to automate tasks such as spec translation, design verification, product validation, and test log root cause analysis for components and system-level products.
· Establish and promote Best Known Methods (BKMs) for deploying LLMs and agentic systems in production environments, ensuring reliability, efficiency, and maintainability.
· Benchmark and evaluate model performance using structured evaluation frameworks, and continuously refine models through prompt tuning, RLHF, and feedback loops.