The role involves designing, building, and scaling AI-driven operational intelligence and agentic systems for large-scale enterprise and cloud environments. The engineer will apply machine learning, predictive modelling, and agentic workflows to address operational challenges such as anomaly detection, root cause analysis, and predictive remediation. Responsibilities include managing the full lifecycle of data, models, agents, services, and deployments. The position focuses on creating production AI services, APIs, and data pipelines that transform unstructured operational signals into structured features. Additionally, the role requires developing RAG-based retrieval systems and implementing observability and drift monitoring for AI services.

Candidates must possess 4 to 11 years of hands-on engineering experience with a strong foundation in backend and distributed systems. Required technical expertise includes machine learning, predictive modeling, generative AI, agentic workflows, and Python programming. Applicants must have a solid grounding in statistics and experience with cloud-native engineering, including containerization and CI/CD practices. A Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, Applied Mathematics, or a related field is required.