Drive AI/ML advancements within our finance division, with a particular emphasis on document processing and analysis.
Requirements
Expert-level proficiency in Python.
Deep understanding and hands-on experience with Transformers, LangChain, Llama 3, Llama 4, Gemini, GPT-4+, and other relevant libraries.
Proficiency in NumPy, Pandas, SciPy, Scikit-learn, TensorFlow, PyTorch.
Expertise in designing and implementing RAG pipelines using vector databases like Postgres, Pinecone, Weaviate, Faiss, and Chroma.
Experience with PDF parsing libraries, chunking techniques, re-ranking algorithms, and table transformers. Strong NLP skills for text extraction, analysis, and understanding from documents.
Proficiency in SQL and dynamic SQL for relational databases (e.g., PostgreSQL, MySQL, SQL Server).
Familiarity with major cloud providers (AWS, Azure, GCP) and MLOps tools like MLflow, Kubeflow, and CI/CD pipelines.
Responsibilities
Design, develop, and deploy production-ready GenAI solutions for various financial applications, such as risk assessment, fraud detection, personalized financial advice, and automated reporting, with a focus on leveraging document-based information.
Work with both structured and unstructured financial data, including complex documents (PDFs, etc.), implementing techniques like NLP, information retrieval, and document parsing for data processing and analysis.
Design and implement Retrieval Augmented Generation (RAG) pipelines, leveraging expertise in vector databases and prompt engineering, specifically for document retrieval and analysis.
Develop and deploy chatbot solutions and other conversational AI interfaces, integrating them with existing financial systems and document processing workflows.
Utilize dynamic SQL to interact with relational databases, extracting and manipulating data for integration into GenAI pipelines, including data extracted from documents.
Write high-quality, well-documented, and testable Python code, adhering to best practices for software development.
Evaluate and integrate emerging GenAI technologies and tools, including the latest LLMs like Llama 3, Gemini, and exploring emerging models like Llama 4.
Other
Highly skilled GenAI Tech Specialist
Demonstrable experience in implementing real-world GenAI products, particularly those involving structured and unstructured data[database & documents].
