GenAI Developer

Key Responsibilities:
Leverage Generative AI (GenAI) models for document understanding, summarization, and intelligent automation.
Build and deploy OCR-driven solutions using AWS Textract, Azure Form Recognizer, Tesseract, and OpenCV.
Explore and apply advanced concepts such as Agentic AI and multi-agent collaboration platforms (MCP) in practical use cases.
Should be able of integrating GenAI/OCR solution into automated workflows.
Design and deploy scalable AI solutions across AWS, Azure, or hybrid cloud infrastructures.
Preprocess and manage structured and unstructured data (e.g., scanned PDFs, forms, tables, images).
Experience in AI model deployment, performance monitoring, and retraining strategies.
Translate business requirements into measurable data science problems and present insights clearly to stakeholders.

Skills and Attributes for Success:
Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
Minimum 3-5 years of hands-on experience in Data Science/ managing AI application Cloud platforms
Deep expertise in OCR tools like AWS Textract, Azure Form Recognizer, and Tesseract.
In-depth knowledge of machine learning, deep learning, and Generative AI prompt engineering techniques.
Strong grasp of NLP technologies and models like GPT, BERT, Transformers.
Proficiency in cloud platforms (AWS and/or Azure), including services like Lambda, SageMaker, Azure ML., azure language service
Knowledge of intelligent document processing workflows.
Knowledge of trusted AI practices: fairness, explain ability, and responsible AI governance.
Strong problem-solving skills and ability to translate complex ideas into technical solutions.
A track record of innovation, staying updated with emerging trends like Agentic AI, MCP, etc

Preferred / Good-to-Have Skills:
Exposure to Agentic AI, multi-agent systems, or autonomous AI orchestration frameworks.
Familiarity with MLOps/DevOps tools: Docker, Kubernetes, CI/CD, Git.
Experience in AI model deployment, performance monitoring, and retraining strategies.
Implementation of monitoring/logging tools for AI pipeline reliability.
Contributions to open-source AI/ML projects or research publications