Solutions Architect SR/ AI/ML Engineer

  • Washington D.C., DC
  • Posted 13 hours ago | Updated 13 hours ago

Overview

Hybrid
Up to $70
Contract - W2
Contract - 12 Month(s)
No Travel Required

Skills

CNN
LLM
LoRA
python
AWS
Azure
FastAPI
HTML/CSS/JavaScript
PyTorch
pandas
NumPy
seaborn

Job Details

Job Title: IT App Solutions Architect SR/ AI/ML Engineer
Location: Washington DC / Hybrid
Duration: 12+ Months
 Key Technical Skills and Responsibilities
  • 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.
  • Generative AI: Develop and integrate solutions powered by LLMs and open-source foundation models. Evaluate and optimize model performance, latency, and cost. Stay current with advances in foundation models, prompt engineering, fine-tuning techniques (LoRA, PEFT), and model safety practices.
  • Modern Code Development: Write efficient, maintainable Python code (advanced Python required), using tools like JupyterLab and VSCode for development and testing. Package and deploy solutions using Docker and Kubernetes 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.
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