Generative AI Engineer

Required skills: 2 to 3 years of experience in GenerativeAI. Python programming skills, especially in AI/ML model development, API integration, and cloud-based deployment. Craft, test, and optimize AI prompts to improve LLM (Large Language Model) performance. Experience with Vector Databases (e.g. AlloyDB, Aurora PostgreSQL DB) – Knowledge of retrieval-augmented generation (RAG) techniques and efficient AI memory storage. Description: We are seeking a Generative AI Engineer with a strong technical background and hands-on experience in Python programming. This role requires expertise in developing, implementing, and optimizing Gen AI-driven solutions using cloud platforms, particularly AWS services such as Bedrock. As a GenAI Engineer, you will be at the forefront of integrating cutting-edge AI capabilities into scalable and efficient solutions that meet evolving business needs. Your primary focus will be to develop, test, and refine AI-driven applications, ensuring they are robust, efficient, and aligned with industry best practices. Key Responsibilities: Develop, test, and deploy Generative AI models using LangChain, AWS Bedrock, and other AI frameworks. Leverage Vector Databases (e.g., AlloyDB, Aurora PostgreSQL DB) to enhance AI search efficiency, knowledge retrieval, and retrieval-augmented generation (RAG) for improved contextual responses. Design effective prompts to optimize AI-generated outcomes through prompt engineering. Implement search and retrieval techniques, including code ingestion and knowledge retrieval to enhance AI performance. Debug AI models and cloud-based solutions independently, particularly within AWS environments. Build and integrate frontend applications using Streamlit and backend services using Python to create end-to-end AI applications. Collaborate with cross-functional teams to test AI-driven solutions, gather feedback,and enhance system capabilities. Communicate complex AI concepts effectively to non-technical stakeholders, ensuring clarity in AI adoption and implementation. Actively contribute to team discussions, providing insights and proposing solutions to AI-related challenges.