Job Description

Stafflevel MLOps Engineer

Position Overview

We are seeking a Stafflevel MLOps Engineer to architect, build, and operationalize the deep learning platform for largescale computer vision systems. This role is ideal for someone who can work independently, define technical direction, and build endtoend ML pipelines from the ground up.

You will be responsible for the full lifecycle of ML systems data ingestion, training, deployment, monitoring, observability, and ongoing operations while collaborating closely with deep learning researchers, software engineers, and product teams.

This Staff engineer will act as a technical authority, mentor other engineers, and establish engineering excellence across the org.

Remote Visual Inspection (RVI) systems enable highprecision, noncontact inspection of critical industrial components using advanced imaging, optics, and AIdriven analytics. In this role, you will help shape the next generation of intelligent inspection capabilities by architecting the machine learning platform that powers automated defect detection and measurement in challenging environments. You will build the endtoend infrastructure that enables largescale ingestion, training, deployment, and monitoring of computer vision models used in highspeed visual inspection workflows. This position combines deep expertise in MLOps, cloud platforms, and computer vision systems to ensure that inspection models are reliable, scalable, and continuously improving ultimately enabling accurate, realtime evaluation of assets using cuttingedge camera and sensor technologies.

Responsibilities MLOps Platform Ownership (Stafflevel)

- Define and own the overall MLOps architecture for deep learning systems across the organization.




- Design and implement endtoend ML pipelines for data ingestion, training, validation, deployment, and monitoring.
- Build and maintain CI/CD pipelines for automated model training, evaluation, and deployment.
- Establish model serving infrastructure, including scalable and reliable realtime or batch inference pipelines.
- Implement model monitoring, data drift detection, performance observability, and alerting frameworks.
- Ensure reliability, scalability, and reproducibility of ML workflows and experiments.
- Manage model versioning, artifact storage, and experiment tracking (MLflow, Kubeflow, TFX, etc.).
- Define and enforce ML specific CI/CD standards and operational best practices.

Data Engineering for ML
- Design and maintain a data aggregation and data ingestion solution for largescale vision datasets.
- Build data pipelines, feature stores, and dataset validation frameworks.
- Contribute to the development and improvement of the computer vision data lake and storage systems.

Computer Vision Deep Learning
- Design, develop, and optimize CV models for detection, segmentation, classification, and tracking.
- Collaborate with algorithm and deep learning teams to transition RD models into productiongrade pipelines.

Cloud Infrastructure
- Work with cloud platforms (AWS, GCP, or Azure) to deploy scalable ML systems.




- Build training and inference solutions using Azure ML / AWS SageMaker / GCP Vertex AI.
- Implement containerized ML services using Docker and Kubernetes.

CrossTeam Collaboration Leadership
- Mentor junior engineers and guide teams as the technical authority for MLOps and ML lifecycle management.
- Collaborate closely with algorithm developers, CV engineers, data engineers, and platform teams.
- Champion engineering excellence, reliability, and automation.

Requirements Core Technical Skills
- Bachelors/Masters degree in Computer Science, Engineering, or related field.
- 7+ years of experience in MLOps, Computer Vision, and Python (stafflevel contribution expected).
- Robust understanding of ML workflow orchestration, lifecycle management, and platform design.
- Advanced Python skills and or proficiency in C++

Deep Learning CV
- Handson experience with PyTorch, TensorFlow, scikitlearn.
- Strong experience in building and deploying production CV systems.

MLOps Data
- Experience with MLflow, Kubeflow, TFX, DAGbased workflow engines (Airflow, Prefect, etc.).
- Experience designing data ingestion pipelines, dataset management systems, and feature stores.
- Familiarity with vector DBs, searchandretrieval systems, and document stores.

Cloud Deployment
- Handson experience with Azure ML, AWS SageMaker, or equivalent production ML platforms.
- Strong understanding of Docker, Kubernetes, GitLab CI/GitHub Actions.

Soft Skills
- Demonstrated technical leadership on complex ML systems.
- Excellent problemsolving, communication, and collaboration skills.
- Ability to operate independently and drive architectural decisions.

📌 MLOps Engineer (Karnataka)
🏢 Wabtec
📍 Karnataka