AI/ML Engineers

.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

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  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.