General Summary:
Role Overview
We are seeking a Senior Full‑Stack Developer to support advanced engineering and analytics initiatives within a semiconductor product environment. The successful candidate will design and deliver scalable big‑data analytic applications used across product, manufacturing, quality, and reliability engineering workflows. This role requires strong technical depth, excellent cross‑team communication, and the ability to leverage modern AI tools to accelerate engineering productivity and drive efficient problem‑solving.
Key Responsibilities
Architect, design, and develop full‑stack systems for high‑volume data and analytics applications supporting semiconductor product and operations teams.
Build robust backend services, microservices, and APIs to enable secure, scalable processing of engineering and manufacturing datasets.
Develop user‑centric and intuitive front‑end interfaces for data visualization, yield/quality dashboards, and analytic workflows.
Design and optimize data models across SQL and NoSQL platforms used for device, product, and test data.
Collaborate closely with cross‑functional partners including product and test engineering and IT team.
Use AI/LLM‑based tools for code acceleration, documentation, data analysis, and engineering‑vibe synthesis (e.g., summarizing complex discussions, generating prototypes, analyzing large datasets).
Provide clear communication of design decisions, tradeoffs, and architectural recommendations to technical and non‑technical stakeholders.
(Plus) Contribute to agentic AI application design, including autonomous data processing agents, agent workflows, or LLM‑tool integrations.
Required Qualifications
7–10 years of professional full‑stack development experience.
Strong backend expertise in one or more of: Node.js, Python.
Proficiency in modern front‑end frameworks (React, Angular).
Strong foundational understanding of big‑data system architecture, distributed computing, and data pipelines.
Hands‑on experience with SQL databases (PostgreSQL, MySQL, MS SQL) and NoSQL systems (MongoDB, DynamoDB, Redis).
Experience deploying systems on cloud environments (Azure, AWS, or GCP).
Excellent communication skills with ability to explain complex concepts to engineers, managers, and cross‑functional teams.
Demonstrated use of AI tools to enhance software engineering output, analytics velocity, or workflow automation.
Preferred Qualifications
Experience designing agentic AI systems or LLM‑integrated applications.
Familiarity with semiconductor engineering data (yield, parametric, test logs, product data, reliability data).
Experience with Kafka, RabbitMQ, or other event-driven systems.
Strong system design, architecture, and documentation skills.
Minimum Qualifications:
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