Roles and Responsibilities
Core AI/ML Model Development
- Design, develop, and implement robust and scalable traditional ML models and advanced AI solutions.
- Conduct thorough data exploration, feature engineering, and experimentation to identify optimal modelling approaches.
- Evaluate and optimize model performance, ensuring high accuracy, reliability, and efficiency.
Computer Vision and Document Intelligence
- Lead the development of Computer Vision solutions, including object detection, image segmentation, and classification, to solve practical business problems.
- Implement and fine-tune models for Optical Character Recognition (OCR) to extract and digitize text from various document types and images.
- Apply image processing techniques (e.g., noise reduction, normalization, geometric transformations) to preprocess data and improve model performance.
Large Language Model (LLM) Application Development
- Lead the research, prototyping, and building of applications using LLMs (e.g., RAG systems, summarization, conversational AI).
- Develop robust strategies for fine-tuning, prompt engineering, and grounding LLMs to meet specific business needs and improve domain-specific performance.
MLOps and Production Delivery
- Lead the effort for ML model building and delivery (for Vision, LLM, and traditional models) into production environments.
- Develop, deploy, and maintain new, scalable, and reproducible ML pipelines (CI/CD for ML/AI systems) using frameworks like Kubeflow or Vertex AI Pipelines.
- Utilize Docker extensively to containerize all ML/AI services and ensure consistent execution across environments.
- Deeply leverage Google Cloud Platform (GCP) and Vertex AI for managing the ML lifecycle, including training, experimentation, model registry, and endpoint deployment.
Programming and Software Engineering
- Apply advanced Python programming skills to write high-quality, efficient, and well-tested code for all production AI/ML systems.
- Adhere to software engineering best practices, including version control (Git), code reviews, and comprehensive documentation.
Required Qualifications
Experience
- Minimum 5 years of professional experience in an AI/ML Engineer, Data Scientist, or equivalent role, with a strong focus on deploying models and AI systems to production.
- Demonstrable experience building applications using LLMs and related frameworks (e.g., LangChain, LlamaIndex).
- Proven experience in Computer Vision, OCR, and image processing
Technical Skills
- Expert proficiency in Advanced Python programming and scientific libraries (e.g., NumPy, pandas, scikit-learn, OpenCV, TensorFlow/PyTorch).
- Strong hands-on experience in implementing and maintaining new ML pipelines.
- In-depth knowledge and experience with Docker for containerization.
- Solid practical experience with Google Cloud Platform (GCP) and direct experience with Vertex AI (Training, Workbench, Endpoints, Generative AI features).
- Experience with libraries and tools specific to vision tasks (e.g., OpenCV, Pillow).
Education
- Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related quantitative field.
