Work Flexibility: Hybrid

• Design, develop, and deploy AI-powered applications leveraging Generative AI, Large Language Models (LLMs), Computer Vision, Machine Learning, and Agentic AI technologies.
• Build scalable backend services, APIs, and workflow orchestration components for AI solutions.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, intelligent assistants, and workflow automation solutions.
• Develop Computer Vision and OCR solutions for image analysis, document digitization, information extraction, and automation workflows.
• Train, optimize, evaluate, and deploy machine learning and deep learning models for production use.
• Build data pipelines supporting AI model training, validation, and inference workflows.
• Develop cloud-native AI applications and deploy solutions on Azure and other cloud platforms.
• Implement MLOps best practices including model versioning, monitoring, CI/CD, and deployment automation.
• Collaborate with cross-functional teams to translate business and clinical requirements into scalable AI solutions.
• Create technical documentation, architecture diagrams, validation reports, and deployment artifacts.
• Stay current with advancements in AI, Computer Vision, LLMs, and Agentic AI.

• Bachelor's Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Electrical Engineering, or a related field with 3+ years of relevant industry experience; OR
• Master's Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Electrical Engineering, or a related field.
• Strong programming skills in Python.
• Experience with machine learning and deep learning frameworks such as PyTorch or TensorFlow.
• Experience developing APIs, backend services, and distributed applications.
• Familiarity with Computer Vision techniques including OCR, image classification, segmentation, and object detection.
• Understanding of LLMs, Prompt Engineering, Retrieval-Augmented Generation (RAG), and Agentic AI concepts.
• Experience working with structured and unstructured data and building data pipelines.
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Knowledge of databases, version control systems, CI/CD pipelines, and software development best practices.
• Familiarity with Docker, Kubernetes, and MLOps concepts.

• Experience with Azure AI Services, Azure OpenAI, Azure Machine Learning, or similar AI platforms.
• Experience with LangChain, LangGraph, Semantic Kernel, AutoGen, or equivalent AI frameworks.
• Experience building production-grade GenAI, RAG, OCR, or intelligent document processing solutions.
• Experience with MLflow, monitoring tools, and model lifecycle management.
• Exposure to healthcare, medical imaging, or regulated environments.
• Experience creating technical architecture documentation and contributing to solution design discussions.