Sr. AI/ML Solution Architect

Job Title: IT Application Solutions Architect Senior (Sr. AI/ML Solution Architect)

Duration: 12+ Months Contract (Possibility for extension)

Location : Washington DC 20001 (Onsite)

Key Technical Skills and Responsibilities:

• AI/ML Development: Design and implement supervised and unsupervised machine learning models, including regression, classification, clustering, time-series forecasting, and boosting methods. Build and fine-tune neural networks such as CNNs, RNNs, and LSTMs.
• Generative AI: Develop and integrate solutions powered by large language models (LLMs) and open-source foundation models. Evaluate and optimize model performance, latency, and cost. Stay current with advancements in foundation models, prompt engineering, fine-tuning techniques (LoRA, PEFT), and model safety best practices.
• Modern Code Development: Write efficient, maintainable Python code (advanced proficiency required). Use tools such as JupyterLab, Databricks, and VS Code for development and testing. Package and deploy solutions using Docker containers on cloud platforms like AWS and Azure. Utilize Git for version control and follow software engineering best practices.

Skills & Experience:

• Model Management and Deployment: Manage MLOps and the 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. Develop user-facing AI tools using Streamlit and front-end technologies (HTML/CSS/JavaScript).
• Platform Enablement: Leverage Databricks expertise to drive platform adoption and accelerate development of new use cases, supporting model automation, AutoML, and template-based development.
• Hands-On Expertise: Strong background in advanced data processing, visualization, and storytelling. Proficient in AI/ML open-source libraries including scikit-learn, PyTorch, pandas, polars, NumPy, seaborn, and related tools 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 objectives into scalable technical solutions.
• Collaboration & Mentorship: Partner with cross-functional teams and mentor junior engineers and data scientists to improve data quality, solution accessibility, self-service capabilities, governance, and enterprise adoption of AI/ML best practices.