Overview

Senior Engineer, IAM, Chennai

Responsibilities

• Senior Engineer, IAM, Chennai
• NielsenIQ is looking for an individual who can join our Identity and Access Management (IAM) Program. We are evolving our enterprise Identity and Access ecosystem by embedding AI and machine learning to improve security posture, automation, and user experience at scale. To support this effort, we are seeking a Senior AI/ML Engineer with strong experience in architecting, designing, building, and operationalizing machine learning systems in complex, distributed, and cloud-based environments.
• The ideal candidate is a self-driven problem solver who enjoys working at the intersection of AI/ML, Cybersecurity, Cloud platforms, and automation, and who wants to be part of growing NIQ global IAM program.
• Lead the design and evolution of the IAM technology stack, ensuring adherence to top-tier security practices across architecture, implementation, monitoring, maintenance, and enhancements.
• Design, build, and evolve AI/ML solutions that analyze identity, access, and behavioral data to detect anomalies, reduce risk, and automate identity security services to enforce IAM, PAM and Secrets Management controls.
• Lead the end-to-end ML lifecycle: data ingestion, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement.
• Architect and implement scalable ML pipelines using Python, cloud-native services, APIs, and infrastructure-as-code.
• Partner with Cybersecurity and other Engineering teams to integrate ML models into enterprise IAM, PAM, and Secrets Management workflows and drive roadmap decisions and enterprise-scale adoption and enforcement.
• Apply advanced security engineering principles to analyze, design, develop, deploy, and support complex software solutions and infrastructure upgrades, setting standards for best practices.
• Develop and deploy real-time and batch inference services for identity and access pattern recognition.
• Apply advanced ML techniques (anomaly detection, clustering, classification, graph-based models, etc.,) to human and non-human identity use cases.
• Design model monitoring, drift detection, and performance metrics aligned to security KPIs and business outcomes.
• Leverage cloud platforms (Azure, AWS, GCP) for distributed training, model serving, and data processing.
• Drive the adoption of MLOps best practices, including CI/CD for models, versioning, reproducibility, and observability.
• Collaborate with product owners and stakeholders to translate security problems into well-defined ML use cases.
• Communicate technical concepts, trade-offs, and results clearly to both technical and non-technical audiences.
• Mentor junior engineers and help establish Cybersecurity engineering standards for applied AI across the organization.

Qualifications

• Bachelor’s degree in Cybersecurity, Computer science, Computer Information Systems, Data Science, or a related field (Master’s preferred).
• 7+ years of software engineering experience, with 4+ years focused on architecting and designing AI/ML systems at global scale (Cybersecurity preferred).
• Strong proficiency in Python and experience building ML systems using modern frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
• Hands-on experience with data pipelines, APIs, and distributed systems.
• Broad knowledge of IAM capabilities, identity stores, authentication/authorization, strong authentication, privileged access methodologies, and Zero Trust principles
• Experience deploying and operating ML models in cloud environments (Azure, AWS, GCP).
• Practical knowledge of MLOps practices, including model versioning, CI/CD, monitoring, and automation.
• Experience working with identity, security, or behavioral data (IAM, access logs, telemetry, or similar domains).
• 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 a strong plus.
• Ability to design solutions that balance accuracy, scalability, explainability, and operational reliability.
• Strong analytical thinking and problem-solving skills in ambiguous, real-world environments.
• Excellent verbal and written communication skills, with the ability to influence cross-functional teams.
• Proven ability to work independently while driving complex initiatives across large organizations.
• Ability to effectively prioritize and execute tasks in a fast-paced environment, balancing risk reduction with business objectives.
• Exceptional verbal and written communication skills, capable of translating complex technical concepts into clear, actionable insights for diverse stakeholders.
• Strong ability to work autonomously while influencing cross-functional teams and have proven track record of driving adoption of security best practices across large organizations.
• Flexible working environment
• Volunteer time off
• LinkedIn Learning
• Employee-Assistance-Program (EAP)