Lead Developer - GenAI & RAG Systems

Lead Developer – GenAI & RAG Systems

Location: Austin, TX (Hybrid with 3 days onsite) Type: Contract Only local candidates

Job Summary: We are seeking a highly skilled Lead Developer with strong expertise in Python, Generative AI (LLMs, RAG pipelines, Embeddings), and GCP Cloud Services. The ideal candidate will have hands-on experience in building production-grade AI/ML systems with UI integration, managing secure enterprise deployments, and ensuring scalability and compliance.

Key Responsibilities

• Design, build, and deploy Retrieval-Augmented Generation (RAG) pipelines for enterprise GenAI solutions
• Develop scalable LLM-based applications using embeddings, vector databases, and prompt engineering best practices
• Work with Azure Functions, Azure OpenAI, Azure ML, Cosmos DB, and Blob Storage for cloud-native implementations
• Build robust Python microservices for real-time AI inference and data processing
• Integrate secure authentication mechanisms (SSO, OAuth, JWT) ensuring security and compliance standards
• Collaborate with front-end engineers to build interactive UIs for AI workflows
• Lead and mentor junior developers in AI/ML engineering best practices
• Ensure performance, fault tolerance, and observability in deployed applications

Required Skills & Experience

• 8+ years of experience in software engineering, with at least 3+ years in AI/ML systems
• Expertise in Python and hands-on experience with RAG pipelines, LLMs (GPT, Claude, LLaMA, etc.), and embedding models
• GCP stack: Vertex AI, Cloud Functions, Firestore, BigQuery
• Deep understanding of enterprise integrations including SSO, authentication, data privacy, and compliance
• Experience with vector databases like Pinecone, FAISS, Weaviate, or Azure Cognitive Search
• Familiarity with front-end/UI development frameworks (e.g. React, Streamlit, Flask for dashboards)
• Proven record of deploying production-grade AI applications with UI and backend integration

Preferred Skills

• Experience with LangChain, LlamaIndex, or similar GenAI orchestration frameworks
• Knowledge of MLOps practices and tools (e.g., MLflow, Azure DevOps)
• Familiarity with CI/CD pipelines and containerization using Docker & Kubernetes