ML Ops Engineer, Model Accuracy

Jobtailor; Bengaluru; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Jobtailor in Bengaluru, India seeks an experienced ML Platform Engineer to design and build scalable backend systems optimized for ML workloads. The role emphasizes strong DevOps practices, modular design, automation, and cost-aware cloud infrastructure with Kubernetes.

You will lead end-to-end MLOps pipelines, manage model lifecycle automation, and collaborate with stakeholders to drive ML platform strategy and adoption.

Qualifications

• Strong foundation in software engineering using Java/Python/Go.
• Expertise in DevOps practices including CI/CD, Docker, Kubernetes, and IaC.
• Proven experience with MLOps frameworks and model lifecycle management.
• Deep understanding of model accuracy, evaluation metrics, and monitoring strategies.
• Hands-on experience with cloud platforms (GCP/AWS/Azure).
• Prior experience managing engineering teams.

Responsibilities

• Design and build scalable backend and platform systems optimized for ML workloads.
• Enforce modular design, automated testing, code quality, and scalability standards.
• Develop and manage cloud-native infrastructure with Kubernetes, containers, and microservices, optimizing for cost and resilience.
• Establish and scale CI/CD pipelines across application and ML lifecycles with observability.
• Architect and implement end-to-end MLOps pipelines from data ingestion to deployment, monitoring, and retraining.
• Drive automation in model lifecycle management including versioning, experiment tracking, reproducibility, and governance using MLflow, Kubeflow, and Airflow.
• Define, monitor, and improve model performance using metrics with evaluation and A/B testing frameworks.
• Lead cross-functional teams and collaborate with stakeholders to deliver scalable AI solutions and shape ML platform strategy.

Skills

Java

Python

Go

DevOps

CI/CD

Docker

Kubernetes

Infrastructure as Code

MLOps

Tools

MLflow

Kubeflow

Airflow

Job description

Responsibilities

• Design and build scalable, reliable backend and platform systems optimized for ML workloads
• Enforce strong engineering practices including modular design, automated testing, code quality, and scalability standards
• Develop and manage cloud‑native infrastructure with Kubernetes, containers, and microservices, while optimizing for cost and resilience
• Establish and scale CI/CD pipelines across both application and ML lifecycles, with full observability (logging, metrics, tracing)
• Architect and implement end‑to‑end MLOps pipelines, from data ingestion through deployment, monitoring, and automated retraining
• Drive automation in model lifecycle management including versioning, experiment tracking, reproducibility, and governance using tools like MLflow, Kubeflow, and Airflow
• Define, monitor, and continuously improve model performance using key metrics (accuracy, latency, drift, bias), with robust evaluation and A/B testing frameworks
• Lead cross‑functional teams and collaborate with stakeholders to deliver scalable AI solutions while shaping the ML platform strategy and adoption roadmap

Requirements

• Strong foundation in software engineering (Java/Python/Go)
• Expertise in DevOps practices (CI/CD, Docker, Kubernetes, infrastructure as code)
• Proven experience in MLOps frameworks and model lifecycle management
• Deep understanding of model accuracy, evaluation metrics, and monitoring strategies
• Hands‑on experience with cloud platforms (GCP/AWS/Azure)
• Prior experience managing engineering teams