Job Summary
Develop AI/ML solutions to support cybersecurity, network operations, and enterprise analytics initiatives. Lead the design and delivery of anomaly detection models for identity (Entra ID) and endpoint security (TrendMicro) telemetry. Responsible for end-to-end ML lifecycle — from baselining to explainability.
Key Responsibilities
Develop AI/ML solutions to support cybersecurity, network operations, and enterprise analytics initiatives. Build, test, and deploy machine learning models under the guidance of senior engineers and architects. Analyze large datasets to identify patterns, anomalies, and opportunities for automation and optimization. Support the development of Generative AI solutions, intelligent assistants, and workflow automation capabilities. Collaborate with cybersecurity, IAM, and network engineering teams to understand business requirements and translate them into technical solutions. Participate in code reviews, testing, documentation, and deployment activities. Monitor production models and contribute to ongoing model improvements and operational support. Continuously learn and apply emerging AI/ML technologies, tools, and best practices
Skill Requirements
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field 5–7 years of experience in software engineering, analytics, data science, or AI/ML development Strong programming skills in Python and working knowledge of SQL Experience developing and deploying machine learning models or advanced analytics solutions Understanding of machine learning concepts, data preprocessing, feature engineering, and model evaluation Familiarity with cloud technologies and modern software development practices Familiarity with Agile methodologies and tools such as JIRA Strong analytical thinking and problem-solving skills Effective communication and collaboration skills
Other Requirements
Exposure to cybersecurity, network operations, observability, or IAM environments Familiarity with Splunk, Dynatrace, Cribl, BigPanda, or similar operational intelligence platforms Experience with cloud-based AI/ML services and MLOps practices Exposure to LLMs, prompt engineering, RAG architectures, or AI agent frameworks Experience with streaming data or event-driven systems Experience in automotive or other large-scale enterprise environments