The Role
Design and implement MLOps processes for machine learning model development, deployment, training, evaluation, retraining, monitoring, and feature engineering. Build scalable ML pipelines and manage complete model lifecycles using AWS SageMaker Unified Studio. Support AWS-based ML infrastructure, troubleshoot operational issues, and apply cloud MLOps best practices. Terraform and broader AWS infrastructure experience are preferred.
Summary Generated by Built In
Do you love a career where you Experience, Grow & Contribute at the same time, while earning at least 10% above the market? If so, we are excited to have bumped onto you.
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If you are a Machine Learning Operations (MLOps) Engineer looking for excitement, challenge and stability in your work, then you would be glad to come across this page.
We are an IT Solutions Integrator/Consulting Firm helping our clients hire the right professional for an exciting long-term project. Here are a few details.
Check if you are up for maximizing your earning/growth potential, leveraging our Disruptive Talent Solution.
Role:Machine Learning Operations (MLOps) Engineer/Practitioner
Location: Hyderabad
Work Mode: Hybrid
Relevent Experience: 3-8 Years
Type: Contract to Hire
RequirementsJob Summary
We are looking for an experienced MLOps Practitioner to join our Canada Post project. The ideal candidate should have strong hands-on experience in machine learning operations, model lifecycle management, and AWS-based ML platforms.
- Design and implement MLOps processes for machine learning model development and deployment.
- Work on model training, evaluation, retraining, and monitoring.
- Perform feature engineering and support end-to-end ML workflows.
- Work with core Machine Learning frameworks and tools.
- Build and maintain scalable ML pipelines and model lifecycle processes.
- Utilize AWS SageMaker Unified Studio for ML development and operational workflows.
- Monitor model performance and implement model retraining strategies when required.
- Strong hands-on experience in MLOps.
- Experience with:
- Model Training & Evaluation
- Feature Engineering
- Model Retraining
- Model Monitoring
- Core ML Frameworks
- Model Training & Evaluation
- Strong experience with AWS SageMaker Unified Studio.
- Experience with Terraform.
- Strong understanding of AWS Infrastructure.
- Experience with AWS services related to ML/AI workloads.
- Knowledge of cloud-based MLOps architecture and best practices.
- Strong MLOps hands-on experience with an understanding of the complete ML lifecycle.
- Ability to work independently in a project environment.
- Strong troubleshooting and problem-solving skills.
- L35-level candidates are preferred.
Interested candidates can apply with their updated resume mentioning relevant MLOps and AWS SageMaker experience.
Benefits
Visit us at http://alignity.io/careers. Alignity Solutions is an Equal Opportunity Employer, M/F/V/D.
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Clients Testimonial: Click Here
Skills Required
- 3-8 years of relevant experience
- Strong hands-on experience with MLOps
- Experience with model training and evaluation
- Experience with feature engineering
- Experience with model retraining
- Experience with model monitoring
- Experience with core machine learning frameworks
- Strong experience with AWS SageMaker Unified Studio
- Ability to work independently in a project environment
- Strong troubleshooting and problem-solving skills
- Experience with Terraform
- Strong understanding of AWS infrastructure
- Experience with AWS services related to ML and AI workloads
- Knowledge of cloud-based MLOps architecture and best practices
- L35-level candidates preferred
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The Company
What We Do
Alignity is a Talent Solutions company focused on revolutionizing talent acquisition, employer branding, and performance inspiration to help organizations achieve accelerated growth.
