Job Title: Applied AI Engineer Job Summary: We are seeking a talented and driven Applied AI Engineer to join our growing Data & AI team. This role is ideal for an experienced software engineer with a deep understanding of generative AI technologies and a passion for building scalable, production-grade AI solutions. You will be responsible for designing, prototyping, and deploying GenAI-powered applications, while also contributing to our AI platform architecture across cloud environments such as Google Cloud Platform (GCP) and Microsoft Azure. Key Responsibilities: • Translate business needs into robust, scalable GenAI technical solutions. • Design, prototype, and implement LLM-driven applications using techniques such as RAG, prompt engineering, fine-tuning, and vector search. • Develop APIs and reusable software components in Python to support GenAI applications. • Leverage orchestration frameworks (e.g., LangChain, LlamaIndex, or LangGraph) to deliver dynamic and modular AI workflows. • Collaborate with cross-functional teams to integrate AI capabilities into business applications. • Deploy and monitor GenAI models and pipelines using cloud-native tools, Kubernetes, and serverless architectures. • Continuously evaluate model and system performance, implementing improvements as needed. • Create and maintain technical documentation and support materials for deployed solutions. Working Conditions: • Hybrid work model: Onsite in Houston office 3 days per week. • Open office environment. Minimum Requirements: • Bachelor’s or Master’s degree in Computer Science, AI/ML, or a related field. • 5+ years of software development experience with strong Python skills. • 2–3+ years of hands-on experience building GenAI/LLM-based applications. • Experience developing multi-step agent workflows using LangGraph or similar orchestration frameworks. • Proficient in designing retrieval pipelines: document loaders, chunking strategies, embedding models, and vector database integration. • Strong grasp of GenAI concepts, including: • Retrieval-Augmented Generation (RAG) • Embeddings & vector databases (e.g., FAISS, Pinecone, ChromaDB) • Prompt engineering and fine-tuning • LLM APIs (e.g., OpenAI, Claude, Gemini) • Experience deploying cloud-native solutions using GCP and/or Azure. • Solid understanding of API design, microservices, and software architecture patterns. • Familiarity with version control systems (e.g., Git, Azure DevOps). • Experience with Docker and Kubernetes. • Demonstrated ability to build and scale AI/ML solutions from proof-of-concept to production. Preferred Qualifications: • Experience with GenAI orchestration tools (e.g., LangChain, LlamaIndex). • Familiarity with DevOps practices: Azure DevOps, YAML pipelines, Terraform. • Strong interpersonal and communication skills, with the ability to collaborate across teams and influence decision-making. • Self-starter with a proactive mindset and strong problem-solving abilities. • Ability to work both independently and in collaborative team environments. • Interest in leveraging AI tools to enhance productivity and solution delivery.