Description

Responsibilities
Architect and Develop AI/ML Solutions: Design, implement, and deploy advanced supervised and unsupervised models (regression, classification, clustering, time-series forecasting, boosting methods) and complex neural networks (CNNs, RNNs, LSTMs).
Lead Generative AI Initiatives: Develop and integrate solutions powered by LLMs and open-source foundation models, applying expertise in prompt engineering, fine-tuning techniques (LoRA, PEFT), and model optimization for performance, latency, and cost.
Implement MLOps and Deployment Pipelines: Manage the full model lifecycle and deployment strategy, including model serialization (Pickle, Joblib, ONNX), containerization with Docker and Kubernetes, and building secure, scalable endpoints using FastAPI and serverless functions.
Champion Platform Enablement: Drive adoption and utilization of the Databricks platform to accelerate use case development, promote model automation, facilitate AutoML, and create reusable template-based solutions.
Adhere to Software Engineering Excellence: Write highly efficient, maintainable Python code (advanced Python skills required), utilizing tools like JupyterLab and VSCode, and enforce Git version control and best practices for testing and quality assurance.
Develop User-Facing AI Applications: Build front-end tools and prototypes using Streamlit alongside standard front-end technologies (HTML/CSS/JavaScript) to demonstrate AI capabilities to business users.
Provide Technical Leadership & Mentorship: Collaborate effectively with cross-functional teams, mentor junior engineers and data scientists, and establish governance standards for data quality, solution accessibility, and business adoption of AI/ML practices.

Qualifications
Advanced proficiency in Python (specifically for machine learning) and extensive experience with core AI/ML open-source libraries, including scikit-learn, PyTorch, pandas, polars, NumPy, and seaborn.
Proven experience designing and deploying end-to-end AI/ML systems, with a strong emphasis on MLOps principles and tools (Docker, Kubernetes, Git).
Deep expertise in developing and optimizing Generative AI solutions using LLMs and foundation models, including hands-on experience with fine-tuning (e.g., LoRA) and performance optimization.
Expertise in cloud platform deployment and infrastructure management on major cloud providers (AWS and/or Azure).
Strong functional knowledge of Databricks for data processing, platform management, and accelerating AI/ML development.
Experience in data processing, feature engineering, advanced visualization, and communicating complex insights effectively through storytelling.
Demonstrated Systems Thinking approach to problem-solving, with the ability to translate high-level business goals into secure, scalable, and viable technical architectures.
Excellent communication, collaboration, and mentorship skills, with a track record of driving best practices and team improvement

Key Skills

Education

Any Gradute