Join NTT DATA, a global leader in technology services, and contribute to our mission of accelerating client success through responsible innovation. We are seeking a Senior Site Reliability & AIOps Platform Engineer specializing in Applied ML & LLM to enhance our enterprise infrastructure. This role involves designing, building, and operationalizing ML and data foundations to transform our operations into predictive, self-healing platforms.

You will work at the forefront of distributed systems reliability, data engineering, and AI, aiming to eliminate operational toil and alert fatigue. Your efforts will involve defining telemetry models, deploying ML models for noise reduction, and architecting LLM-powered incident intelligence. Partnering with platform and infrastructure teams, you will accelerate Mean Time to Resolution (MTTR), enforce Service Level Objectives (SLOs), and scale automation under strict governance.

Key responsibilities include architecting canonical data models and ingestion pipelines for unified operational data, and engineering automated data extraction for supervised model training.

You will design and deploy ML models for alert noise scoring, risk forecasting, and event suppression, alongside implementing unsupervised learning for anomaly detection and cascading failure interception.

This role also involves driving the MLOps lifecycle, building agentic operations workflows using LLMs and RAG for dynamic ticket enrichment, and developing automated incident response tools.

Furthermore, you will embed reliability engineering principles into AI pipelines, lead the operational rollout of AI tools through staged governance, and drive post-incident analysis to refine algorithms.

A minimum of 5 years of progressive experience in Site Reliability Engineering, Machine Learning Engineering, or Data Platform Engineering at an enterprise scale is required. You must have a proven track record of shipping production-grade ML models and data pipelines.

Strong software engineering fundamentals in Python and production ML frameworks like scikit-learn and pandas/numpy are essential. Deep data engineering experience, including designing canonical schemas and building streaming/batch ingestion pipelines, is also crucial.

Practical understanding of core SRE principles (SLIs/SLOs, error budgets, incident management, observability) and strong technical communication skills to lead cross-functional architecture reviews are necessary.

Preferred qualifications include hands-on experience with Graph ML, building production LLM-assisted workflows, familiarity with enterprise observability pipelines, and experience managing containerized workloads on Kubernetes.