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Tier-1 brand plus metro location but niche AIOps observability specialization limits applicant density.
Observability and AIOps skills transfer across industries but banking regulatory and platform context adds constraints.
Specific seniority, mandatory AIOps/MLOps and observability tool experience make filters strict.
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Lead development and management of the bank’s central monitoring and observability platform, enabling stakeholders to troubleshoot, analyze performance, and conduct capacity planning.
Own the Predictive Monitoring, Predictive Observability, and AIOps practices, using machine learning to predict issues early and enhance observability capabilities.
Ensure stability, reliability, and compliance with governance and risk management frameworks, including participation during weekend releases and major incidents for predictive capability enablement.
8-10 years relevant experience as Observability/Monitoring Specialist, AIOps Specialist, Data Transformation Lead, or similar role.
Proven hands-on experience with machine learning frameworks (TensorFlow, PyTorch, SKLearn, XGBoost) and programming languages (Python, Hive, Spark, PySpark).
Experience in AIOps and MLOps including creation of data ingestion ETL pipelines and working with Gen-AI LLM models (e.g., Mistral, Llama, Bert).
Bachelor’s degree in Computer Science, Information Systems or equivalent; certifications in Machine Learning and AIOps.
Experienced leader capable of managing and mentoring teams, structuring product backlogs, and driving observability strategies aligned with business goals.
Deep technical expertise in Observability technologies (Elastic, Grafana) combined with advanced AI/ML application for predictive monitoring.
Comfortable operating in Agile delivery frameworks with strong risk management, governance adherence, and participation in critical incident response activities.