Sr. Machine Learning Engineer - Bellevue, WA

Sr. Machine Learning Engineer

Location: Bellevue, WA

Position Type: Contract

US Citizen, Green Card, TN, GC EAD and H4 EAD only. No third-party agencies corp to corp.

Skill Sets:

• Enterprise-scale technical experience with AI technologies (Azure Machine Learning, Azure cognitive services, Open-Source AI frameworks, Python, MLOps, cloud and hybrid infrastructures and architecture designs)
• Hands on experience creating AI Proof of Concepts, Minimum Viable Products (MVPs) for customers
• Identify and implement complex intelligent rules to maintain high effectiveness based on analysis of the issues
• Manage and constantly improve the intelligent systems and models by improving data used for training
• Define processes and automation for scaling to large number of models
• Define and manage effectiveness and quality metrics

Job Description:

Work within a complex BI / Reporting function in the Finance domain.

Apply technical knowledge to architect and design solutions that meet business and IT needs, create AI roadmaps, drive POCs and MVPs, and ensure long term technical viability of new deployments, infusing key AI technologies where appropriate (e.g. Azure Client, Azure Cognitive Services, Azure Bot Service, Azure Databricks, Azure Synapse, Power BI, etc.)

Create Proof of concepts (POCs) / Minimum Viable Products (MVPs) etc. through to production deployment and operationalization of projects.

Identify and implement complex intelligent rules to maintain high effectiveness based on analysis of the issues.

Manage and constantly improve the intelligent systems and models by improving data used for training.

Define processes and automation for scaling to large number of models.

Define and manage effectiveness and quality metrics.

Design, prototype, implement and test descriptive, predictive analytics, forecasting, and causal inference models

Explore and correlate large data sets to uncover gaps

Work with data engineers to architect and develop operational models that run at scale

Partner with teams to identify and explore opportunities for the application of machine learning and predictive analysis

Communicate with technical and non-technical audiences, and contribute modeling expertise as a team player

Identify and implement complex intelligent rules to maintain high effectiveness based on analysis of the issues.

Manage and constantly improve the intelligent systems and models by improving data used for training.

Define processes and automation for scaling to large number of models.

Define and manage effectiveness and quality metrics.

Participate actively in requirement gathering, detailed design, code reviews, and bug discussions and drive discussions around appropriate solutions.

Ability to work independently.

Interaction with team members in different time zones to handover / transition work progress and vice versa