AI/ML Engineer - Local to Dallas who can attend Face to Face Interview

We are looking for a highly skilled AI Engineer with expertise in building advanced AI-driven applications using AWS Bedrock and large language models (LLMs). The ideal candidate will have hands-on experience in prompt engineering, agent building, and Conversational AI Generation (CAG) to create intelligent, context-aware AI agents. You will leverage Python and FastAPI to develop scalable APIs, implement Retrieval-Augmented Generation (RAG), and work with vector databases to optimize AI-driven search and response systems. This role requires strong collaboration with cross-functional teams to deliver innovative AI solutions on AWS., Design, develop, and deploy AI applications leveraging AWS Bedrock and other AWS AI/ML services. Engineer effective prompts and prompt templates to optimize LLM performance and contextual understanding. Build and maintain intelligent AI agents capable of multi-turn conversations and task automation using Conversational AI Generation (CAG) techniques. Develop scalable APIs using Python and FastAPI to serve AI models and conversational agents. Implement Retrieval-Augmented Generation (RAG) frameworks to enhance knowledge retrieval and response accuracy. Manage and optimize vector databases (e.g., Pinecone, Weaviate) for semantic search and similarity matching. Design and apply scoring and ranking algorithms to improve LLM output relevance and user experience. Collaborate closely with data scientists, software engineers, and product managers to integrate AI capabilities into products. Optimize AI workflows and infrastructure on AWS for scalability, security, and cost-efficiency. Stay current with emerging AI technologies, prompt engineering best practices, and AWS innovations.

Do you have experience in Python?, Proven experience with AWS Bedrock and AWS AI/ML ecosystem. Strong proficiency in Python programming, with practical experience using FastAPI for API development. Expertise in prompt engineering to design, test, and refine prompts for LLMs. Experience building AI agents and conversational AI systems using CAG methodologies. Working knowledge of Retrieval-Augmented Generation (RAG) and its application in AI solutions. Hands-on experience with vector databases such as Pinecone, Weaviate, or similar platforms. Familiarity with scoring and ranking techniques for large language model outputs. Solid understanding of AWS cloud infrastructure components including IAM, Lambda, S3, and EC2. Excellent collaboration skills within agile, cross-functional teams. Strong analytical and problem-solving abilities. Effective communication skills to convey complex AI concepts clearly. Preferred Qualifications:

Experience with Hugging Face Transformers or similar LLM frameworks. Knowledge of containerization (Docker) and orchestration tools (Kubernetes). Familiarity with CI/CD pipelines for AI/ML model deployment. Background in NLP, dialogue systems, or human-computer interaction.