Summary:

The Senior ML Ops Engineer is responsible for defining and owning the MLOps architecture for deep learning systems across the organization. This role involves designing and implementing end-to-end ML pipelines, building and maintaining CI/CD pipelines, and establishing model serving infrastructure. The engineer will ensure reliability, scalability, and reproducibility of ML workflows and experiments while managing model versioning and artifact storage.

Responsibilities:

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.
Design and maintain a data aggregation and data ingestion solution for large-scale vision datasets.
Build data pipelines, feature stores, and dataset validation frameworks.
Collaborate with algorithm and deep learning teams to transition R&D models into production-grade pipelines.
Work with cloud platforms to deploy scalable ML systems.
Build training and inference solutions using Azure ML, AWS SageMaker, or GCP Vertex AI.
Implement containerized ML services using Docker and Kubernetes.
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.

Requirements:

Bachelor&rsquos/Master&rsquos degree in Computer Science, Engineering, or related field.
7 years of experience in MLOps, Computer Vision, and Python.

Required Skills:

Strong understanding of ML workflow orchestration, lifecycle management, and platform design.
Advanced Python skills and proficiency in C.
Hands-on experience with PyTorch, TensorFlow, scikit-learn.
Experience with MLflow, Kubeflow, TFX, DAG-based workflow engines.
Experience designing data ingestion pipelines, dataset management systems, and feature stores.
Hands-on experience with Azure ML, AWS SageMaker, or equivalent production ML platforms.
Strong understanding of Docker, Kubernetes, GitLab CI/GitHub Actions.

Preferred Skills:

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

#AditiConsulting
# 26-03493

Senior ML Ops Engineer

EY - GDS Consulting - AIA - AI ML Ops Engineer - Senior