Senior Engineer with Python, Agentic AI, Multi-cloud, MCP along with IAM, Cyber experience

2 days 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 identity security 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 in partnership 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, and security KPIs aligned with business outcomes.
Use Azure, AWS, and GCP for distributed training, model serving, and data processing.
Drive MLOps practices including model CI/CD, versioning, reproducibility, automation, and observability.
Translate security problems into ML use cases, communicate technical trade-offs, mentor junior engineers, and establish applied-AI cybersecurity standards.

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.
Strong Python proficiency and experience with PyTorch, TensorFlow, or scikit-learn.
Hands-on experience with data pipelines, APIs, and distributed systems.
Knowledge of IAM capabilities, identity stores, authentication and authorization, strong authentication, privileged access methodologies, and Zero Trust principles.
Experience deploying and operating ML models in Azure, AWS, or GCP environments.
Practical knowledge of MLOps, model versioning, CI/CD, monitoring, and automation.
Experience working with identity, security, behavioral data, IAM, access logs, or telemetry.
Familiarity with REST APIs, Git-based workflows, containerization, and infrastructure-as-code.
Understanding of non-human identities, service accounts, secrets, and automation-driven access patterns is preferred.
Ability to balance accuracy, scalability, explainability, and operational reliability while driving complex initiatives across large organizations.
Strong analytical, communication, prioritization, problem-solving, and cross-functional influencing skills.