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AI/ML Engineer - AI Observability(ML Ops Engineer)
Hiring for IT Consulting Firm (MNC)
AI/ML Engineer - AI Observability(ML Ops Engineer)
TalentOne HR Consulting LLP's logo

AI/ML Engineer - AI Observability(ML Ops Engineer)

at Hiring for IT Consulting Firm (MNC)

5 - 10 yrs
₹20L - ₹30L / yr
Pune, Nagpur
Skills
MLOps
DevOps
Artificial Intelligence (AI)
skill iconData Science
Data engineering
TensorFlow
PyTorch
Scikit-Learn
CI/CD
Azure

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 
  • Strong 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 
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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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