ML Ops Engineer - DSA
vConstruct Private Limited (company site, opens in a new tab)8–10 yrsPuneOn-siteDirect career pageEst. 20–35 LPA
Posted on 16 Sept
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Job description
Structured overview of role & requirements
About This Role
- Implement scalable, cloud-native AI observability solutions for reliable and maintainable AI/ML systems in construction technology.
- Standardize observability practices (logging, metrics, tracing, model monitoring) and lead automation-first DevOps/MLOps with infrastructure-as-code and CI/CD pipelines for ML workflows.
- Design and manage DataOps pipelines with automated data quality and anomaly detection; monitor model KPIs and ensure governance including bias detection and explainability.
Minimum Requirements
- 8-10 years experience in DevOps, MLOps, Data Engineering, Software Engineering, or Site Reliability Engineering.
- Bachelor’s degree in Computer Science, Data Science, Information Systems, or related field.
- Strong experience with cloud infrastructure (preferably Azure) and containerized ML workload deployment.
- Proficiency in at least one object-oriented programming language (preferably Python) with experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn).
Ideal Candidate Profile
- Experienced in leading AI/ML observability and MLOps practices in production environments with large-scale, complex systems.
- Skilled at designing reusable, scalable patterns and automation-first infrastructure-as-code CI/CD pipelines for ML workflows.
- Familiar with governance and compliance aspects of AI including bias detection, explainability, auditability, and performance validation.