Work Flexibility: Hybrid

What You Will Do

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.

What You Will Need

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.

Travel Percentage: None