Data Science and ML Manager

As a GenAI Applications Developer, your main responsibility will be to drive the development of enterprise-level applications using LLM frameworks such as Langchain, Autogen, and Hugging Face. You will architect intelligent pipelines utilizing PySpark, TensorFlow, and PyTorch within Databricks and AWS environments. Your role will involve implementing embedding models and managing VectorStores for retrieval-augmented generation (RAG) solutions. Additionally, you will integrate and leverage MDM platforms like Informatica and Reltio to supply high-quality structured data to ML systems. Using SQL and Python, you will be responsible for data engineering, data wrangling, and pipeline automation. Collaboration with data scientists, engineers, and product teams will be essential to scope, design, and deploy AI-powered systems. It will also be crucial for you to ensure model governance, version control, and auditability that align with regulatory and compliance expectations. **Qualifications Required:**

• Master's degree with 8 - 10 years of experience in Data Science, Artificial Intelligence, Computer Science, or related fields
• Bachelor's degree with 10 - 14 years of experience in Data Science, Artificial Intelligence, Computer Science, or related fields
• Diploma with 14 - 16 years of hands-on experience in Data Science, AI/ML technologies, or related technical domains As a GenAI Applications Developer, your main responsibility will be to drive the development of enterprise-level applications using LLM frameworks such as Langchain, Autogen, and Hugging Face. You will architect intelligent pipelines utilizing PySpark, TensorFlow, and PyTorch within Databricks and AWS environments. Your role will involve implementing embedding models and managing VectorStores for retrieval-augmented generation (RAG) solutions. Additionally, you will integrate and leverage MDM platforms like Informatica and Reltio to supply high-quality structured data to ML systems. Using SQL and Python, you will be responsible for data engineering, data wrangling, and pipeline automation. Collaboration with data scientists, engineers, and product teams will be essential to scope, design, and deploy AI-powered systems. It will also be crucial for you to ensure model governance, version control, and auditability that align with regulatory and compliance expectations. **Qualifications Required:**