Sr. Associate AI ML Engineer

• Develop analytics, machine learning models, and agentic AI solutions to address security risks, improve detection capabilities, and enhance the organization’s cybersecurity posture.

• Design, build, and evaluate AI agents and multi-agent workflows that support cybersecurity use cases such as automated threat detection, alert triage, incident investigation, vulnerability analysis, and response orchestration.

• Apply generative AI, large language models, retrieval-augmented generation, and agent-based architectures to automate security analysis, summarize findings, recommend actions, and support human-in-the-loop decision-making.

• Collaborate with Data Engineers to translate security-focused algorithms, AI agents, and automation workflows into scalable, production-ready solutions.

• Design and implement security-focused analytics pipelines leveragingMLOpspractices, generative AI, and agent-based architectures (e.g, autonomous agents for threat detection and response). 

• Collaborate with cybersecurity teams toidentifyopportunities where AI agents can reduce manual effort, accelerate investigation timelines, and improve consistency in security operations.

• Contribute to data engineering efforts to refine data infrastructure and ensure scalable, efficient security analytics. 

• Generate reports, annotated code, and other projects artifacts to document, archive, and communicate your work and outcomes. 

• Share and discuss findings with team members practicingSAFeAgile delivery model. 

Functional Skills:

Basic Qualifications:

• Master’s degree OR Bachelor’sdegree and 5 to 9 years of experience with one or more analytic software tools or languages (e.g., SAS, SPSS, R, Python)

Preferred Qualifications:

• Experience with one or more analytic software tools or languages (e.g., SAS, SPSS, R, Python) 

• Experience applying generative AI and agent-based systems to cybersecurity use cases (e.g., automated threat detection, response orchestration, and security analysis) 

• Demonstrated skill in the use of applied analytics, descriptive statistics, featureextractionand predictive analytics on industrial datasets 

• Strong foundationin machine learning algorithms and techniques 

• Experience in statistical techniques and hypothesis testing, experience with regression analysis,clusteringand classification