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.
A variety of soft skills and experience may be required for the following role Please ensure you check the overview below carefully.
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.
ADK is MUST
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. xsgimln
Interest in leveraging AI tools to enhance productivity and solution delivery.