AI/ML Engineer

Lumen Solutions Group Inc.•Washington, DC

17d•Remote

About The Position

AI/ML Development: Design and implement supervised and unsupervised models including regression, classification, clustering, time-series forecasting, and boosting methods. Build and fine-tune neural networks including CNNs, RNNs, and LSTMs. Modern Code Development: Write efficient, maintainable Python code (advanced Python required), using tools like JupyterLab, Databricks and VSCode for development and testing. Package and deploy solutions using Docker containers on cloud platforms like AWS and Azure. Use Git for version control and champion SWE best practices. Model Management and Deployment: Manage MLOps and full model lifecycle. Serialize and manage models using Pickle, Joblib, and/or ONNX. Deploy models using FastAPI and serverless functions, building secure and scalable endpoints. Create user-facing AI tools using Streamlit and front-end technologies (HTML/CSS/JavaScript). Platform Enablement: Databricks expertise to drive platform adoption and accelerate the development of new use cases, supporting model automation, AutoML, and template-based development. Hands-on: Advanced data processing, visualization, and storytelling. Solid background in popular AI/ML open-source libraries including scikit-learn, PyTorch, pandas, polars, NumPy, seaborn, and other libraries for data cleaning, feature engineering, and visualization. Systems Thinking: Approach problems with an end-to-end mindset, considering model performance, data quality, infrastructure, user experience, and downstream applications. Translate business goals into viable, scalable technical solutions. Collaboration & Mentorship: Work closely with cross-functional teams and mentor junior engineers and data scientists for the overall improvement of data quality metrics, solution accessibility, self-service capabilities, governance, and business adoption of AI/ML best practices.

Requirements

Advanced Python required
Experience with JupyterLab, Databricks, and VSCode
Experience with Docker containers
Experience with cloud platforms like AWS and Azure
Experience with Git for version control
Experience with MLOps and full model lifecycle management
Experience with Pickle, Joblib, and/or ONNX for model serialization
Experience with FastAPI and serverless functions
Experience with Streamlit
Experience with front-end technologies (HTML/CSS/JavaScript)
Databricks expertise
Advanced data processing, visualization, and storytelling skills
Solid background in popular AI/ML open-source libraries including scikit-learn, PyTorch, pandas, polars, NumPy, seaborn, and other libraries for data cleaning, feature engineering, and visualization.
Systems Thinking approach

Responsibilities

Design and implement supervised and unsupervised models including regression, classification, clustering, time-series forecasting, and boosting methods.
Build and fine-tune neural networks including CNNs, RNNs, and LSTMs.
Write efficient, maintainable Python code (advanced Python required), using tools like JupyterLab, Databricks and VSCode for development and testing.
Package and deploy solutions using Docker containers on cloud platforms like AWS and Azure.
Use Git for version control and champion SWE best practices.
Manage MLOps and full model lifecycle.
Serialize and manage models using Pickle, Joblib, and/or ONNX.
Deploy models using FastAPI and serverless functions, building secure and scalable endpoints.
Create user-facing AI tools using Streamlit and front-end technologies (HTML/CSS/JavaScript).
Utilize Databricks expertise to drive platform adoption and accelerate the development of new use cases, supporting model automation, AutoML, and template-based development.
Perform advanced data processing, visualization, and storytelling.
Approach problems with an end-to-end mindset, considering model performance, data quality, infrastructure, user experience, and downstream applications.
Translate business goals into viable, scalable technical solutions.
Work closely with cross-functional teams.
Mentor junior engineers and data scientists for the overall improvement of data quality metrics, solution accessibility, self-service capabilities, governance, and business adoption of AI/ML best practices.