Position Overview

The AI Observability Engineer will be instrumental in implementation of scalable, cloud-native solutions to meet the growing needs of our Data & Development team. The successful candidate will demonstrate the ability to abstract complexity and create reusable, scalable patterns that accelerate development. The AI Observability Engineer will build and maintain a robust framework to ensure the reliability and maintainability of DPR Construction's complex AI systems.

Responsibilities

- Standardize observability practices across AI/ML and other development teams including logging, metrics, tracing, and model performance monitoring, ingesting data from multiple platforms
- Lead hands-on implementation of automation-first DevOps and MLOps practices, enabling infrastructure-as-code and consistent, repeatable environment provisioning
- Design and manage intelligent DataOps pipelines with automated data quality monitoring and anomaly detection
- Deploy, maintain and monitor containerized ML workloads
- Extend existing CI/CD pipelines to support automated infrastructure changes and ML workflows
- Implement AI-driven data validation, schema and concept drift detection and metadata management.
- Establish governance frameworks for AI systems, including bias detection,



explainability, and auditability
- Extend existing Azure RBAC strategy by automating role and permission management to reduce manual intervention
- Develop automated test suites for model performance, regression, edge cases and bias validation
- Monitor model KPIs (accuracy, precision, recall, latency, calibration)
- Ensure reproducability of experiments and production models
- Act as a technical point of contact for DevOps and MLOps practices, developing reusable patterns, documentation, and proof-of-concepts to drive adoption

Qualifications

- Bachelor’s degree in computer science, Data Science, Information Systems, or a related field
- 5+ years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering
- Solid understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure
- Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn

Skills:- Data Science, DevOps, MLOps, Data engineering, TensorFlow, PyTorch, Scikit-Learn, CI/CD and Azure

📌 AI/ML Engineer - AI Observability(ML Ops Engineer) (Pune)
🏢 TalentOne HR Consulting
📍 Pune