1 day ago
Chennai, IndiaSenior
Responsibilities
- Lead the design and evolution of the IAM technology stack across architecture, implementation, monitoring, maintenance, and enhancements.
- Build AI/ML solutions that analyze identity, access, and behavioral data to detect anomalies, reduce risk, and automate IAM, PAM, and secrets-management controls.
- Own the end-to-end ML lifecycle, including data ingestion, feature engineering, model development, evaluation, deployment, monitoring, and improvement.
- Architect scalable ML pipelines and real-time or batch inference services using Python, cloud-native services, APIs, and infrastructure-as-code.
- Integrate ML models into enterprise IAM, PAM, and secrets-management workflows with cybersecurity and engineering teams.
- Apply anomaly detection, clustering, classification, and graph-based models to human and non-human identity use cases.
- Design model monitoring, drift detection, performance metrics, reproducible workflows, and MLOps practices.
- Use Azure, AWS, and GCP for distributed training, model serving, and data processing.
- Translate security problems into ML use cases, communicate technical results to varied audiences, mentor junior engineers, and establish applied-AI cybersecurity standards.
Requirements
- Bachelor’s degree in Cybersecurity, Computer Science, Computer Information Systems, Data Science, or a related field; a master’s degree is preferred.
- At least 7 years of software engineering experience, including at least 4 years architecting and designing AI/ML systems at global scale; cybersecurity experience is preferred.
- Strong Python proficiency and experience with PyTorch, TensorFlow, or scikit-learn for ML systems.
- Hands-on experience with data pipelines, APIs, distributed systems, and deploying and operating ML models in cloud environments.
- Knowledge of IAM, identity stores, authentication and authorization, strong authentication, privileged access methodologies, Zero Trust principles, and identity or behavioral data.
- Practical knowledge of MLOps, model versioning, CI/CD, monitoring, automation, REST APIs, Git-based workflows, containerization, and infrastructure-as-code.
- Understanding of non-human identities, service accounts, secrets, and automation-driven access patterns is a strong plus.
- Ability to balance accuracy, scalability, explainability, and operational reliability while driving complex initiatives independently across large organizations.
- Strong analytical, problem-solving, prioritization, communication, and cross-functional influence skills.
Benefits
- Flexible working environment.
- Volunteer time off.
- LinkedIn Learning.
- Employee Assistance Program (EAP).