Principal AI/ML Engineer

HarmonyTech•Northern, VA

2d•Onsite

About The Position

HarmonyTech is currently looking for a talented and self-motivated Principal AI/ML Engineer specializing in the technology stack given below. You will be working with a high-performing agile team leading, designing and implementing AI/ML and business intelligence solutions for a Federal Regulatory agency. Position is available in Northern VA, requires US citizenship and ability to obtain public trust clearance.

Requirements

Bachelor’s degree in computer science or a related field
Minimum 5 years of hands-on experience designing, implementing and maintaining AI/ML models out of which at least 2 years with AWS Sagemaker.
Hands-on experience with Python and Machine learning libraries such as TensorFlow and Keras.
Experience with data analysis, data cleansing techniques, feature engineering, model design, model validation, with good understanding of model metrics
Experience with one or more of the following types of models – Linear regression, Logistic regression, Time series analysis and forecasting
At least 5 years of experience as a hands-on lead developer, developing code and managing projects using required technology stack (see below).
Expert knowledge of machine learning and deep learning algorithms and Python libraries
Expert knowledge of Microsoft Power BI and Power Automate
At least 2 years of hands-on experience in Power Automate, Power BI and DBeaver
Ability to communicate clearly with business users, the team, and leadership
Innovative, results-oriented and focused on delivering on time and with quality

Nice To Haves

Experience working with regulatory agencies such as OCC, SEC or FDIC
Experience with Agile SDLC and tools
Experience with methodologies such as CRISP-DM

Responsibilities

Lead an agile technical team developing AI/ML and Business Intelligence solutions.
Business Analysis & Project Support – requirements elicitation, process mapping, gap analysis, user story development, risk/issue/dependency tracking
Business Transformation – use-case documentation, data sensitivity/compliance assessments, stakeholder interviews, change management
Systems & Technology Analysis – architecture/dependency mapping, integration analysis, configuration management, workflow diagnostics
AI/ML Testing, Validation & Documentation – test plan execution, model inventories, model cards, explainability artifacts, drift/anomaly monitoring, AI governance documentation
Solution Design & Development – translating requirements into technical specs, workflow automation builds, data transformation design.