Overview
On Site
Contract - W2
Contract - 6 Month(s)
Skills
Art
Optimization
Evaluation
Workflow
Deep Learning
Internationalization And Localization
Computer Vision
Docker
Linux
Computer Science
Machine Learning (ML)
Research
OWL
Python
PyTorch
Kubernetes
Ubuntu
Machine Learning Operations (ML Ops)
Amazon SageMaker
Microservices
Real-time
Health Insurance
Insurance
Team Building
Collaboration
Wiki
Knowledge Base
Status Reports
Account Management
IT Consulting
Managed Services
Recruiting
Artificial Intelligence
Cyber Security
Enterprise Architecture
Training
FOCUS
Job Details
Bring cutting-edge vision to life as a highly skilled Computer Vision Pipeline Engineer! This dynamic role combines deep learning expertise with infrastructure engineering to develop, experiment with, and deploy state-of-the-art CV models. You'll be at the core of building scalable training, evaluation, and deployment pipelines that power real-world computer vision applications.
Key Responsibilities:
- Deploy large-scale computer vision models, including YOLO and OWL, optimizing for performance and efficiency.
- Design and implement robust training pipelines and deployment frameworks using tools such as Kubernetes and Ubuntu-based environments.
- Perform advanced hyperparameter tuning and model optimization to meet accuracy, latency, and compute requirements.
- Build reusable tools for model evaluation and monitoring within an MLOps workflow.
- Collaborate with cross-functional teams to integrate models into scalable production systems and automate the lifecycle of machine learning models.
- Stay current with the latest CV research (e.g., foundation models, prompt-based detection) and apply insights into model development.
Required Skills & Qualifications:
- Proficiency in Python and deep learning frameworks, especially PyTorch.
- Strong hands-on experience with YOLO (You Only Look Once) and OWL (Open-World Localization) object detection models.
- Solid understanding of computer vision tasks: object detection, segmentation, classification.
- Experience deploying models using Kubernetes, Docker, and AWS SageMaker.
- Familiarity with MLOps tools such as MLflow or Weights & Biases.
- Expertise in Ubuntu/Linux environments for ML/AI experimentation and deployment.
- Demonstrated success in hyperparameter tuning for large models.
- Bachelor's or Master's in Computer Science, Machine Learning, or a related field. A PhD or research experience is a plus.
Key Skills:
- YOLO, OWL.
- Python, PyTorch.
- Kubernetes, Ubuntu.
- Hyperparameter Tuning.
- MLOps.
- AWS SageMaker.
- Event-Driven Microservices.
Preferred Qualifications:
- Experience with real-time inference and edge deployment (e.g., TensorRT, ONNX Runtime, Jetson).
- Familiarity with distributed training frameworks.
Required Education:
- Bachelor's Degree or equivalent.
Benefits:
- 401(k).
- Dental Insurance.
- Health insurance.
- Vision insurance.
- We are an equal-opportunity employer and value diversity, equality, inclusion, and respect for people.
- The salary will be determined based on several factors, including, but not limited to, location, relevant education, qualifications, experience, technical skills, and business needs.
Additional Responsibilities:
- Participate in OP monthly team meetings and participate in team-building efforts.
- Contribute to OP technical discussions, peer reviews, etc.
- Contribute content and collaborate via the OP-Wiki/Knowledge Base.
- Provide status reports to OP Account Management as requested.
About us:
OP is a technology consulting and solutions company, offering advisory and managed services, innovative platforms, and staffing solutions across a wide range of fields - including AI, cybersecurity, enterprise architecture, and beyond. Our most valuable asset is our people: dynamic, creative thinkers who are passionate about doing quality work. As a member of the OP team, you will have access to industry-leading consulting practices, strategies & and technologies, innovative training & education. An ideal OP team member is a technology leader with a proven track record of technical excellence and a strong focus on process and methodology.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.