Req ID: RQ173820

Type of Requisition: Regular

Clearance Level Must Be Able to Obtain: None

Public Trust/Other Required: None

Job Family: Solutions Architect

Skills:

Business Intelligence (BI),Data Science,ETL,Feature Engineering,Machine Learning Algorithms

Experience:

4 + years of related experience

Job Description:

GDIT is seeking an AWS SageMaker/Analytics Solutions Architect to join our team to help grow the accounts across the Department of Health and Human Services (HHS). As a solutions architect, you will work hands-on with the team to develop technical solutions that drive innovation.

This is a hybrid position, but the candidate is required to be within an hour commuting distance to the various HHS clients who are located within the DC/Maryland/Virginia area.

Responsibilities:

• Build and configure end-to-end MLOps pipeline on AWS cloud for model management, model deployment & service and model governance using AWS SageMaker. Use Amazon SageMaker Studio for development and tracking.

• Implement CI/CD pipelines using GITLAB to automate model deployment and updates, enabling rapid iterations and reducing time-to-market.

• Create Framework for deploying Client models to production environments using SageMaker endpoints and set up monitoring to track model performance and drift over time.

• Set up and run SageMaker Clarify bias analysis through Amazon Sagemaker Experiments to check the model for potential biases.

• Setup SageMaker Model Monitor to allow clients to select data from a menu of options such as prediction output, and capture metadata such as timestamp, model name, and end point so that clients can analyze model predictions based on the metadata.

• Create framework for Model governance.

• Use Model Dashboard from SageMaker Studio for implementing model governance solutions.

• Maintain logs for reproducibility, validation, conformity, and auditability.

• Use SageMaker model registry for model management and model tracking.

• Add Cohort model explainability used in the deployment phase, specifically in the model validation step before deployment.

• Implement static deployment strategies (using traffic routing patterns) to deploy Client model(s) - Blue/Green, A/B

• You will design, implement, test, document, and support cross-cutting services to help customers do machine learning at scale.

• You'll assist in gathering and analyzing business and functional requirements, and translate requirements into technical specifications for robust, scalable, supportable solutions that work well within the overall system architecture.

Required Skills:

• Bachelors Degree and at least 5 years of AWS Analytics and Sagemaker experience

• Experienced in developing Data Integration and advanced analytics solutions.

• Machine Learning and Data Science: Strong understanding of machine learning algorithms, Data preprocessing techniques and feature engineering.

• AWS Sagemaker: In-depth knowledge of Amazon Sagemaker services including SageMaker Studio, CI/CD/CT with AWS Sagemaker and GIT.

• Model management and model tracking using Sagemaker model registry.

• Model governance framework, maintaining logs for reproducibility, validation, conformity, and auditability.

• Prior experience deploying machine learning models with AWS.

• Familiarity machine learning operations (MLOps) best practices for deployment and monitoring.

• Expertise with AWS services supporting data science and analytics (EC2, Lambda, Sagemaker, Glue)

• Proficiency in Python and R

The likely salary range for this position is $127,500 - $172,500. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.

Our benefits package for all US-based employees includes a variety of medical plan options, some with Health Savings Accounts, dental plan options, a vision plan, and a 401(k) plan offering the ability to contribute both pre and post-tax dollars up to the IRS annual limits and receive a company match.To encourage work/life balance, GDIT offers employees full flex work weeks where possible and a variety of paid time off plans, including vacation, sick and personal time, holidays, paid parental, military, bereavement and jury duty leave. To ensure our employees are able to protect their income, other offerings such as short and long-term disability benefits, life, accidental death and dismemberment, personal accident, critical illness and business travel and accident insurance are provided or available.We regularly review our Total Rewards package to ensure our offerings are competitive and reflect what our employees have told us they value most.

We are GDIT. A global technology and professional services company that delivers consulting, technology and mission services to every major agency across the U.S. government, defense and intelligence community. Our 30,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 30 countries worldwide, offering leading capabilities in digital modernization, AI/ML, Cloud, Cyber and application development. Together with our clients, we strive to create a safer, smarter world by harnessing the power of deep expertise and advanced technology.

We connect people with the most impactful client missions, creating an unparalleled work experience that allows them to see their impact every day. We create opportunities for our people to lead and learn simultaneously. From securing our nation’s most sensitive systems, to enabling digital transformation and cloud adoption, our people are the ones who make change real.

GDIT is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status, or any other protected class.