Summary We are seeking a highly skilled Senior Generative AI Developer to join our AI/ML team. This role focuses on leveraging cutting:edge Generative AI (GenAI) technologies to drive innovation and efficiency in software development and business processes. The ideal candidate will work on advanced techniques such as Retrieval:Augmented Generation (RAG), document chunking, and data embedding across multiple vector databases. They will collaborate closely with cross:functional teams to design, implement, and optimize AI:driven solutions, with exposure to cloud:native AI platforms like Amazon Bedrock and Microsoft Azure OpenAI considered a plus Your role in our mission :Develop and deploy GenAI:based applications to solve complex business problems. :Implement RAG frameworks to enhance information retrieval and response accuracy. :Design and optimize document chunking strategies tailored to specific data types and use cases. :Build and manage data embeddings using various vector databases for high:performance similarity searches. :Collaborate with data engineers and scientists to integrate AI solutions seamlessly into existing pipelines. :Explore and implement best practices for leveraging Amazon Bedrock and Azure OpenAI services. :Stay updated on emerging trends and technologies in the GenAI and AI/ML landscape. What were looking for :Proficiency in Generative AI frameworks and technologies like :Prompt Engineering : creating specific prompts to guide LLM outputs :Fine Tuning and Few:Shot learning : Adapting LLMs for domain:specific tasks. :Retrieval:Augmented Generation (RAG) : Combining LLMs with external data retrieval systems :Text:to:Text Transfer Transformation (T5) : Framework for translating tasks into text forms. :Expertise in designing and implementing chunking strategies for diverse datasets. :Strong knowledge of data embedding techniques and working experience with vector databases like Pinecone, Weaviate, or Milvus. :Solid programming skills in Python, with experience in AI/ML libraries such as TensorFlow, PyTorch, or Hugging Face Transformers. :Familiarity with cloud platforms and services for AI/ML workloads (AWS or Azure). :Experience with API integration for AI services and building scalable applications. What you should expect in this role :Develop and deploy GenAI:based applications to solve complex business problems. :Implement RAG frameworks to enhance information retrieval and response accuracy. :Design and optimize document chunking strategies tailored to specific data types and use cases. :Build and manage data embeddings using various vector databases for high:performance similarity searches. :Collaborate with data engineers and scientists to integrate AI solutions seamlessly into existing pipelines. :Explore and implement best practices for leveraging Amazon Bedrock and Azure OpenAI services. :Stay updated on emerging trends and technologies in the GenAI and AI/ML landscape.