. Overall Focus: Designing, developing, training, deploying, and maintaining AI/ML models, encompassing both classical machine learning techniques and advanced Generative AI/Large Language Models (LLMs). General Responsibilities (Applicable to all AI/ML Engineers): Data preprocessing, feature engineering, model selection, and rigorous hyperparameter tuning. Developing robust and scalable APIs for AI/ML model consumption and integration. Implementing MLOps practices for model monitoring, performance tracking, and automated retraining strategies. Continuously researching and applying advancements in the AI/ML field to project work Required Specialized Skill Sets & Experience: A. Classical Machine Learning (Essential expertise): Deep practical expertise in algorithms such as regression, classification, clustering, anomaly detection, dimensionality reduction, time series analysis, etc. Strong statistical foundation, experimental design, and model evaluation techniques (e.g., cross-validation, precision/recall, ROC/AUC, F1-score). B. Generative AI & LLMs (Essential expertise): Core GenAI Development: Proficiency with open-source GenAI frameworks and libraries (e.g., Hugging Face Transformers, LlamaIndex, Langchain, FAISS). Deep experience in developing and fine-tuning RAG (Retrieval Augmented Generation) based GenAI solutions. Hands-on experience architecting and utilizing major cloud-based GenAI platforms (e.g., AWS Bedrock, Azure OpenAI Service, Google Vertex AI). Applied GenAI Capabilities: Leveraging various GenAI models for tasks such as: Text Processing: Advanced summarization, complex question answering, nuanced text generation, semantic search, knowledge extraction, and high-quality embedding generation. Image Processing (as relevant): Image generation/manipulation, content analysis, object recognition leveraging foundation models. Video Processing (as relevant): Video content analysis, summarization, event detection using GenAI approaches. Fundamental GenAI Understanding: Thorough understanding and practical strategies for addressing bias and hallucination in LLMs. Expertise in vector embeddings , various similarity comparison techniques (e.g., cosine similarity, dot product), and the practical differences between keyword-based vs. semantic search . C. Essential Cross-Cutting Experience (Relevant across the AI/ML team): Document Management & Processing: Strong experience with advanced OCR technologies and document digitization workflows. Proven ability in information extraction from diverse document types (structured, semi-structured, unstructured PDFs, images) and document understanding using ML/AI techniques. D. Desirable (Good to Have) Experience: Agentic AI Experience designing or implementing AI agents or multi-agent systems. MCP (Model Compliance/Criticality/Card Platform - please specify if this means something else in your context ) Relevant Project Experience: Experience aiwith platforms or processes for model governance, documentation, risk assessment, or similar.