Build and optimize Retrieval-Augmented Generation (RAG) architectures leveraging enterprise knowledge sources, vector databases, and search technologies to improve response accuracy and relevance.
Develop integrations with enterprise applications such as SAP, Salesforce, ServiceNow, SharePoint, Microsoft 365, and custom business systems through APIs and middleware platforms.
Establish monitoring, evaluation, and observability frameworks to track model performance, response quality, hallucinations, latency, user adoption, and business value realization.
Perform prompt engineering, workflow orchestration, model fine-tuning, and AI solution optimization to enhance effectiveness, user experience, and cost efficiency.
Deploy AI solutions on Azure cloud and establish reusable design patterns.
Requirements
Master’s or PhD in Computer Science, Data Science, Statistics, or a related field.
7-10 years of experience in AI/data science.
Proven experience deploying AI/ML solutions in production environments.
Strong knowledge of cloud platforms (Azure preferred), MLOps, and data governance.
Excellent communication and stakeholder management skills.
Experience in semiconductor, manufacturing, or industrial domains is a plus.
Preferred Knowledge and Experience
Semiconductor industry experience.
Experience with tools like LangGraph, Azure ML, and Snowflake.
Understanding of AI security and compliance frameworks.
Competencies
Self-motivated, able to multitask, prioritize, and manage time efficiently
Strong problem-solving skills
Data analysis skills. Ability to analyze complex data and turn it into actionable information
Collaboration and teamwork across multiple functions and stakeholders around the globe
Flexibility and adaptability
Drive for results, Able to work under pressure and meet deadlines
