Full Stack AI Engineer

Job Summary

We are seeking an exceptional Full Stack AI Engineer to join our team and drive the development of cutting-edge AI-powered applications. In this role, you will design, build, and deploy production-grade AI systems, with a particular focus on Generative AI, Retrieval-Augmented Generation (RAG), and agentic architectures. You'll work at the intersection of software engineering and artificial intelligence, creating scalable solutions that deliver real impact to the business. You will work directly with business stakeholders to understand requirements and translate them into technical solutions, and you will have full ownership over the projects you build from conception to deployment.

Position to be Hybrid out of Costa Mesa, CA or Atlanta, GA. 

Key Tasks & Responsibilities (Essential Functions)

· Build and Deploy GenAI Solutions: Design, develop, and optimize generative AI applications, including RAG pipelines and agentic systems that leverage Large Language Models (LLMs)

· Machine Learning Development: Build and deploy traditional machine learning models using frameworks like XGBoost, Random Forest, and other ML algorithms based on business requirements and use cases

· Full Stack Development: Create end-to-end AI-powered applications using modern frameworks like FastAPI for backend services and React or Streamlit for intuitive user interfaces

· Production AI Systems: Deploy AI models into production environments and build robust systems that monitor performance, automate retraining, and ensure reliability at scale

· Cloud Architecture: Leverage Azure cloud services to build scalable, secure, and cost-effective AI infrastructure

· LLM Integration: Implement advanced LLM capabilities including prompt engineering, embeddings, fine-tuning, and API integrations to create intelligent applications

· Containerization & DevOps: Design and deploy containerized applications using Docker, ensuring consistent and reproducible deployments across environments

· Problem Solving: Identify, analyze, and resolve complex technical challenges, optimizing for performance, scalability, and exceptional user experience

· Cross-Functional Collaboration: Partner closely with product managers, designers, data scientists, and engineers to deliver innovative AI solutions that meet business objectives

· Technical Communication: Translate complex AI and technical concepts into clear, actionable insights for both technical and non-technical stakeholders

Skills and Minimum Experience Required

· Bachelor's degree in Computer Science, Software Engineering, or related technical discipline with 4+ years of proven software development experience, OR Master's degree with 2+ years of experience

· Strong programming skills in Python and Java, with excellent software design and development capabilities

· Hands-on experience with Generative AI and machine learning frameworks (e.g., TensorFlow, PyTorch, LangChain, LlamaIndex)

· Solid understanding of LLM concepts, including embeddings, prompt engineering, fine-tuning, and model evaluation

· Demonstrated experience building RAG (Retrieval-Augmented Generation) systems and agentic architectures

· Experience with Azure cloud services (or similar cloud services) and cloud-native application development

· Proficiency with containerization technologies, particularly Docker

· Experience building APIs and microservices using FastAPI or similar frameworks

· Front-end development experience with React or Streamlit

· Proven track record deploying AI models into production and building automated monitoring and retraining pipelines

· Strong version control skills using Git and collaborative development workflows

· Ability to work effectively in cross-functional teams and communicate technical concepts clearly

· Familiarity with data pipelines, databases (SQL/NoSQL), and ETL/ELT processes

Preferred Skills and Experience

· Demonstrated experience building applications or features with Large Language Models (LLMs) or AI APIs through professional work, personal projects, open-source contributions, or hackathons

· Experience with additional AI frameworks and tools (e.g., Hugging Face, OpenAI API, Anthropic Claude API)

· Knowledge of vector databases (e.g., Pinecone, Qdrant, Azure AI Search)

· Experience with MLOps practices and tools (e.g., MLflow, Weights & Biases)

· Understanding of Kubernetes and orchestration platforms

· Experience with A/B testing and experimentation frameworks for AI systems

· Knowledge of responsible AI practices, including bias detection, fairness, and safety considerations