REMOTE
Role: IT Application Solutions Architect Senior - AI/ML Engineer
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, 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.
Required Qualifications
∙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.
Thanks
Charan
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