Description

Architect and deploy advanced AI/ML solutions, including supervised/unsupervised models, neural networks, and Generative AI applications, while leading MLOps practices and platform enablement on Databricks.

Responsibilities

• Design and implement complex models (CNNs, RNNs, LSTMs, boosting) and Generative AI solutions using LLMs, applying prompt engineering and fine-tuning techniques (LoRA, PEFT).
• Build and maintain MLOps pipelines for the full model lifecycle, utilizing Docker, Kubernetes, and secure endpoints via FastAPI and serverless functions.
• Develop user-facing AI prototypes and tools using Streamlit, HTML, CSS, and JavaScript to demonstrate capabilities to business stakeholders.
• Champion the adoption of the Databricks platform for data processing, AutoML, and reusable template-based solutions.
• Write efficient, maintainable Python code, enforce Git version control, and mentor junior engineers on data quality and solution governance.

Required Skills

• 5+ years of experience with advanced Python for machine learning, including scikit-learn, PyTorch, pandas, polars, NumPy, and seaborn.
• Proven expertise in designing and deploying end-to-end AI/ML systems with a strong focus on MLOps principles.
• Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for scalable model deployment.
• Deep understanding of cloud infrastructure deployment on AWS and/or Azure.
• Strong functional knowledge of Databricks for data engineering and accelerating AI/ML development.
• Proficiency in front-end technologies (HTML, CSS, JavaScript) for building AI application interfaces.
• Experience with model serialization (Pickle, Joblib, ONNX) and performance optimization for latency and cost.

Preferred Skills

• Experience with time-series forecasting and clustering algorithms.
• Track record of communicating complex insights through data storytelling.

Key Skills

Education

Any Gradute