Posted May 6, 2026
Key Responsibilities:
Qualifications:
Required Skills:
Preferred Skills:
2 or more years experience with developing Robotic Process Automation and/or automation efforts
2 or more years using Azure or AWS cloud services
2 or more years skilled at data visualization tools like Tableau, PowerBI, etc. - 2 or more years with modern data engineering with APIs
2 or more years applying agile SDLC
Experience in enterprise-scale deployments of AI-driven platforms
Contributions to open-source AI/ML projects are a plus (Note: Any additional details of the company present in the JD have been omitted as per the provided instructions) Role Overview: As a Senior AI Engineer at EY Cybersecurity, you will be joining the Cyber Analytics and Data Science team to utilize machine learning, data engineering automation, and Data Science principles to address enterprise challenges and further the Cybersecurity mission. Your role will involve designing, developing, deploying, and automating solutions while collaborating closely with stakeholders to enhance predictive and prescriptive analytics for cybersecurity. Your expertise will be pivotal in leading the implementation of cutting-edge technology and methodologies in partnership with data owners and Cybersecurity stakeholders. You will be expected to demonstrate problem-solving skills, work alongside IT and business partners, and serve as a trusted consultant. Key Responsibilities:
Structure business problems and formulate data-driven hypotheses in collaboration with business partners
Extract and aggregate data from various sources to derive actionable intelligence using advanced data analytics tools
Develop agentic AI systems using frameworks like LangChain, LangGraph, and related libraries
Implement and optimize RAG systems for accurate responses by accessing external knowledge sources
Fine-tune LLMs for task-specific reasoning, planning, and dynamic adaptation
Lead the development of enterprise-grade AI platforms integrating LLMs, RAG embeddings, and agentic AI protocols
Establish best practices for MLOps monitoring and observability to ensure scalable AI solutions
Perform in-depth data analysis including machine learning, classification, optimization, time series analysis, and pattern recognition
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