Seeking a highly skilled and motivated Data Scientist with a strong emphasis on Generative AI and Large Language Models (LLM). This role requires a unique blend of technical expertise and business insight to transition from promising hypotheses and ML experiments to fully-fledged AI/ML products with real-world impact. As a specialist in Generative AI techniques, you will work with innovative closed and open-source Gen AI models, apply standard methodologies in prompt engineering, RAG applications, and fine-tune LLM models to drive business outcomes.
Responsibilities:
- Develop and implement machine learning models, focusing on Generative AI techniques and LLM, to solve sophisticated business problems, using a variety of algorithms and techniques.
- Streamline Generative AI model development and deployment using Azure infrastructure and capabilities like cognitive search and Vectordb, and frameworks such as Langchain, Llama, Semantic Kernel, and Autogen.
- Apply prompt engineering techniques, work with RAG applications, and fine-tune language models to improve their performance in specific tasks.
- Collaborate with multi-functional teams to plan, scope, implement, and sustain predictive analytics solutions.
- Design and complete experiments to validate and optimize machine learning models, ensuring accuracy, efficiency, and scalability.
- Deploy machine learning models and applications on cloud platforms like Azure ML or Databricks, ensuring seamless integration, scalability, and cost-effectiveness.
- Develop measurements and feedback systems, and mentor associates and peers on MLOps & LLMOps standard methodologies.
- Stay up-to-date with the latest advancements in machine learning, generative AI, prompt engineering, RAG applications, and cloud technologies, and apply them to enhance our data science capabilities.
Requirements
Requirements:
- Hands-on experience with Gen AI-related tech stack, including Azure cognitive search, Langchain, Semantic Kernel, and agent frameworks like Autogen, Vectordb.
- Hands on experience with predictive modelling around Credit risk
- Experience in fine-tuning open-source models like Llama, Phi2 is a bonus.
- Experience with Open AI APIs, Fine-tuning and Assistant APIs
- Proven experience as a Data Scientist, with a strong focus on machine learning, generative AI, prompt engineering, and RAG applications.
- Working knowledge of Cloud, Architecture, REST APIs to deploy inferencing and fine-tuning solutions.
- Strong partner management, ability to translate sophisticated technical topics into business language, presentation skills, and an ability to balance a sense of urgency with shipping high-quality and pragmatic solutions.
- Strong software development skills with proficiency in Python and advanced working SQL knowledge, preferably using the Azure stack and experience with Azure ML, Databricks, Azure Data Factory.
- Experience in product-ionizing code through the DevOps pipeline (git, Jenkins pipeline, code scan).
- Solid understanding of machine learning algorithms, statistical modeling, and data analysis techniques.