Senior Machine Learning Engineer

Job Description -

Need LinkedIn profile, Visa copy, and submittal matrix with submittal!

Need 2 official refernces.

Please check with consultant/search over the LinkedIn if they have multiple LinkedIn profile. Because we have submitted multiple consultant and they found many fake consultants!

Need only local!

Need Certification copy as well!

Sr. Machine Learning Engineer

Client: PNC

6 months Contract to Hire

Brecksville, OH or Pittsburgh, PA or Strongsville, OH or Birmingham, AL or Phoenix AZ - HYBRID: 3 days office / 2 remote

Years of Experience: 13 years applicable experience required

Candidate Industry Background: Banking/financial - Recent will be highly prefer

Roles and Responsibilities:

Research, develop, and implement machine learning algorithms for use in software and hardware applications.

Perform application programming activities including coding, testing, debugging, documenting, maintaining, and modifying machine learning systems.

Analyze and review enhancement requests and specifications.

Implement changes to algorithms to improve performance.

Troubleshoot problems to improve user experience.

Provide quality assurance reviews

Perform post-implementation validation of machine learning models and resolve any bugs found during testing

Must have technical skills (Min 8yrs of exp required in all of the below skills)

AWS SageMaker implementation experience

MLOps pipeline development

CI/CD for ML workflows

Python programming

ML framework experience (TensorFlow/PyTorch)

Hybrid cloud architecture

Git version control

MLflow or similar experiment tracking

MLflow based model experiment tracking and model serving

Kubeflow implementation

Apache Airflow orchestration

Terraform/CloudFormation

DVC for data versioning

Feature store implementation or integration with MLOps systems

Education/Certifications:

Bachelor s in computer science, engineering, or related field

AWS Certified Machine Learning Specialty

AWS Solutions Architect certification

AWS Certified DevOps Engineer Professional

Additional Data Science and LLM focused certification will be a plus

Screening Questions:

1, Explain MLOps and key components of that in context of AWS SageMaker or similar experience?

2, Explain an end-to-end MLOps implementation on SageMaker and if the same had to be implemented in a hybrid state?

3, Describe your experience deploying ML models to production using SageMaker inference endpoints and/or using other non-AWS endpoints?

4, Explain what monitoring systems have you implemented across the MLOps value chain?

5, Explain why you should or should not consider MLflow or such tools in conjunction with AWS SageMaker for MLOps in a hybrid architecture?