RT Y Location any Total Experience: 4 yrs, Relevant Experience: 3+ yrs. Mandatory Skills: • 3+ years of experience in AI/ML, NLP, or Conversational AI development, preferably in financial domains. • Proficiency in LLM architectures (GPT, Llama, Claude, etc.) and fine-tuning techniques. • Strong background in Python, TensorFlow, PyTorch, Hugging Face, or similar AI frameworks. • Experience with Object Oriented Programming using Python and its design patterns. Nice to have skills: • Expert in Python Standard libraries - Concurrent Execution, Multiprocessing, Subprocessing, Generic Operating System Services, Unix Specific Services, File Formats. • Experience working with financial datasets, regulatory reporting, or risk analytics. • Hands-on experience with cloud platforms (AWS, Azure, GCP) for AI model deployment. • Knowledge of financial domain-specific NLP techniques (e.g., entity recognition, sentiment analysis, text summarization). • Experience with vector databases, embeddings, andRAG (Retrieval-Augmented Generation) frameworks is a plus. • Understanding of data security, compliance (e.g., SOX, GDPR), and model interpretability in financial AI applications. • Prior experience in financial automation, accounting AI tools, or fintech applications. • Familiarity with LLM Ops, model monitoring, and bias mitigation in AI models." Job Description: • 3+ years of experience in AI/ML, NLP, or Conversational AI development, preferably in financial domains. • Proficiency in LLM architectures (GPT, Llama, Claude, etc.) and fine-tuning techniques. • Strong background in Python, TensorFlow, PyTorch, Hugging Face, or similar AI frameworks. • Experience with Object Oriented Programming using Python and its design patterns. • Expert in Python Standard libraries - Concurrent Execution, Multiprocessing, Subprocessing, Generic Operating System Services, Unix Specific Services, File Formats. • Experience working with financial datasets, regulatory reporting, or risk analytics. • Hands-on experience with cloud platforms (AWS, Azure, GCP) for AI model deployment. • Knowledge of financial domain-specific NLP techniques (e.g., entity recognition, sentiment analysis, text summarization). • Experience with vector databases, embeddings, and RAG (Retrieval-Augmented Generation) frameworks is a plus. • Understanding of data security, compliance (e.g., SOX, GDPR), and model interpretability in financial AI applications. • Prior experience in financial automation, accounting AI tools, or fintech applications. • Familiarity with LLM Ops, model monitoring, and bias mitigation in AI models." - U2XLK2