Job Description: 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, 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.
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