MLOps Engineer

Wabtec; Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

A leading engineering firm is looking for a Staff-level MLOps Engineer to architect and operationalize a deep learning platform for large-scale computer vision systems. This role requires extensive experience in MLOps, cloud platforms, and computer vision, along with the ability to design end-to-end ML pipelines. The successful candidate will take on a technical authority role, mentoring engineers and ensuring the reliability and scalability of machine learning workflows. Join us to shape next-generation intelligent inspection capabilities.

Qualifications

• 7+ years of experience in MLOps, Computer Vision, and Python.
• Hands-on experience with PyTorch, TensorFlow, and scikit-learn.
• Experience designing data ingestion pipelines and dataset management systems.

Responsibilities

• Define and own the overall MLOps architecture for deep learning systems.
• Design and implement end-to-end ML pipelines for data ingestion and deployment.
• Build and maintain CI/CD pipelines for automated model training and deployment.
• Mentor junior engineers and lead cross-team collaborations.

Skills

MLOps

Computer Vision

Python

Cloud platforms

Deep Learning

CI/CD pipelines

Containerization (Docker, Kubernetes)

Education

Bachelors/Masters in Computer Science, Engineering, or related field

Tools

MLflow

Kubeflow

TFX

Airflow

Prefect

Azure ML

AWS SageMaker

GCP Vertex AI

Job description

Position Overview

• We are seeking a Staff-level MLOps Engineer to architect, build, and operationalize the deep learning platform for large-scale computer vision systems. This role is ideal for someone who can work independently, define technical direction, and build end-to-end 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 high-precision, non-contact inspection of critical industrial components using advanced imaging, optics, and AI-driven 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 end-to-end infrastructure that enables large-scale ingestion, training, deployment, and monitoring of computer vision models used in high-speed 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, real-time evaluation of assets using cutting-edge 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 RD 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

• Bachelors/Masters 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.