AI/ML Engineer

Core AI/ML Model Development

• Design, develop, and implement robust, scalable ML models and AI solutions.
• Conduct data exploration, feature engineering, and experimentation to identify optimal modeling approaches.
• Evaluate and fine-tune model performance to ensure accuracy, reliability, and efficiency.

Computer Vision and Document Intelligence

• Lead development of computer vision solutions (object detection, segmentation, classification) for practical business use cases.
• Build and optimize OCR models to extract and digitize text from diverse document types.
• Apply advanced image preprocessing techniques (e.g., noise reduction, normalization, geometric transformations) to enhance model results.

Large Language Model (LLM) Application Development

• Research, prototype, and build applications using LLMs (e.g., RAG systems, summarization, conversational AI).
• Develop fine-tuning, prompt engineering, and grounding strategies to tailor LLMs for domain-specific performance.

MLOps and Production Delivery

• Lead the deployment of ML models (Vision, LLM, traditional) into production environments.
• Build scalable, reproducible ML pipelines using frameworks such as Kubeflow or Vertex AI Pipelines.
• Containerize AI/ML services using Docker for consistent, portable execution.
• Fully leverage Google Cloud Platform (GCP) and Vertex AI for model training, registry, experimentation, and endpoint management.

Programming and Software Engineering

• Write high-quality, efficient, and well-tested Python code for production AI/ML systems.
• Follow best practices in software engineering, including version control (Git), peer reviews, and documentation.

 Essential Skills & Attributes for Success

• Minimum 5 years of professional experience as an AI/ML Engineer, Data Scientist, or similar, with a strong focus on deploying models to production.
• Proven expertise building LLM-based applications using tools such as LangChain and LlamaIndex.
• Demonstrated success in computer vision, OCR, and image processing.
• Advanced proficiency in Python and scientific computing libraries (NumPy, pandas, scikit-learn, OpenCV, TensorFlow, PyTorch).
• Strong hands-on experience building and maintaining ML pipelines.
• Proven ability working with Google Cloud Platform (especially Vertex AI services).
• Solid practical experience with Docker containerization.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or related field.

 Preferable Skills & Attributes for Success

• Experience integrating AI systems within large-scale enterprise or supply chain environments.
• Familiarity with prompt optimization and retrieval-augmented generation (RAG) architectures.
• Strong understanding of CI/CD principles and automated testing for ML pipelines.
• Exposure to multi-cloud or hybrid cloud environments.
• Excellent analytical thinking, problem-solving, and communication skills.