For additional information, please review .* ## 5 years+ of experience in AI/ML development, with a proven track record of building and deploying sophisticated GenAI applications.* ## Deep understanding of GenAI models and architectures, including transformers, LLMs (e.g., Llama, Gemini, GPT-4), GANs, and diffusion models. Familiarity with Agentic AI concepts.* ## Extensive experience with prompt engineering, fine-tuning LLMs, and evaluating their performance.* ## Expert-level Python programming skills and proficiency with relevant libraries (e.g., Transformers, LangChain, TensorFlow, PyTorch, Pandas, NumPy, Scikit-learn, Flask/Django, LlamaIndex).* ## Experience with vector databases (e.g., Pinecone, Weaviate, Chroma, Faiss, PostgreSQL with vector extensions) and implementing RAG pipelines using tools like LlamaIndex and LangChain.* ## Strong software engineering skills, including containerization (Docker, Kubernetes), CI/CD pipelines, and cloud infrastructure management (AWS, Azure, GCP).* ## Strong analytical, problem-solving, and communication skills.* ## Experience with MLOps principles and tools.* ## Excellent collaboration skills.* ## Experience with financial data and applications, particularly in areas like fraud detection, risk management, or personalized financial advice.* ## Strong understanding of financial markets and instruments.* ## Familiarity with chatbot development frameworks and best practices, including conversational AI design and natural language understanding (NLU).* ## Experience leading or contributing to complex data science or AI/ML projects in a fast-paced environment.* ## Publications or presentations at conferences related to AI/ML or GenAI.* ## Experience with data visualization and reporting tools (e.g., Tableau, Power BI, matplotlib, seaborn).* ## Experience with SQL and NoSQL databases.* ## Master's degree or PhD in Computer Science, Engineering, Statistics, or a related field.* ## **Programming Languages:** Python (expert proficiency required), SQL* ## **Python Packages:** Transformers, LangChain, LlamaIndex, TensorFlow, PyTorch, Pandas, NumPy, Scikit-learn, Flask/Django, and other relevant data science, machine learning, and web development libraries.* ## **Deep Learning Frameworks:** TensorFlow, PyTorch* ## **LLMs:** Llama, Gemini, GPT-4, and other advanced LLMs.* ## **Vector Databases:** Pinecone, Weaviate, Chroma, Faiss, PostgreSQL with vector extensions (pgvector).* ## **Cloud Platforms:** AWS, Azure, GCP* ## **MLOps Tools:** MLflow, Kubeflow, or similar.* ## **Containerization:** Docker, Kubernetes* ## **CI/CD Tools:** GitHub Actions, Jenkins, or similar.* ## **Version Control:** Git* ## **Data Visualization & Reporting:** Tableau, Power BI, matplotlib, seaborn.* ## **Databases:** SQL and NoSQL databases.#J-18808-Ljbffr