MLOps Engineer

GENERAL ELECTRIC (GE) in Bengaluru is seeking a Staff MLOps Engineer to architect, build, and operationalize a cutting-edge deep learning platform for large-scale computer vision systems. This role is perfect for an independent thinker who can define technical direction and construct end-to-end ML pipelines from scratch. You will manage the complete lifecycle of ML systems, including data ingestion, training, deployment, monitoring, observability, and ongoing operations, while collaborating closely with deep learning researchers, software engineers, and product teams.

As a Staff Engineer, you will be a technical authority, mentoring other engineers and establishing engineering excellence across the organization. This position is instrumental in shaping the future of Remote Visual Inspection (RVI) systems, which enable high-precision, non-contact inspection of critical industrial components using advanced imaging, optics, and AI-driven analytics. You will help build the next generation of intelligent inspection capabilities by architecting the machine learning platform that powers automated defect detection and measurement in challenging environments.

Your responsibilities will include building the end-to-end infrastructure for large-scale ingestion, training, deployment, and monitoring of computer vision models used in high-speed visual inspection workflows. This role demands deep expertise in MLOps, cloud platforms (AWS, GCP, Azure), and computer vision systems to ensure inspection models are reliable, scalable, and continuously improving, enabling accurate, real-time evaluation of assets using state-of-the-art camera and sensor technologies.

Responsibilities

MLOps Platform Ownership (Staff-level)

• Define and own the overall MLOps architecture for deep learning systems across the organization.
• Design and implement end-to-end 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 real-time 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 large-scale 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 R&D models into production-grade 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.

Cross-Team 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

• Bachelor's/Master's degree in Computer Science, Engineering, or related field.
• 7+ years of experience in MLOps, Computer Vision, and Python (staff-level contribution expected).
• Strong understanding of ML workflow orchestration, lifecycle management, and platform design.
• Advanced Python skills and/or proficiency in C++.

Deep Learning & CV

• Hands-on experience with PyTorch, TensorFlow, scikit-learn.
• Strong experience in building and deploying production CV systems.

MLOps & Data

• Experience with MLflow, Kubeflow, TFX, DAG-based workflow engines (Airflow, Prefect, etc.).
• Experience designing data ingestion pipelines, dataset management systems, and feature stores.
• Familiarity with vector DBs, search-and-retrieval systems, and document stores.

Cloud Deployment

• Hands-on 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 problem-solving, communication, and collaboration skills.
• Ability to operate independently and drive architectural decisions.

Wabtec (mentioned in Additional Information) emphasizes a 'People First' culture and aims to Expand the Possible by continuously improving. This role offers an opportunity to revolutionize how the world moves for future generations.