Lead Developer - GenAI & RAG Systems
Job Title: Lead Developer - GenAI & RAG Systems Location : Austin, TX(Hybrid with 3 days onsite)Type : ContractOnly local candidatesJob SummaryWe 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 ResponsibilitiesDesign, build, and deploy Retrieval-Augmented Generation (RAG) pipelines for enterprise GenAI solutionsDevelop scalable LLM-based applications using embeddings, vector databases, and prompt engineering best practicesWork with Azure Functions, Azure OpenAI, Azure ML, Cosmos DB, and Blob Storage for cloud-native implementationsBuild robust Python microservices for real-time AI inference and data processingIntegrate secure authentication mechanisms (SSO, OAuth, JWT) ensuring security and compliance standardsCollaborate with front-end engineers to build interactive UIs for AI workflowsLead and mentor junior developers in AI/ML engineering best practicesEnsure performance, fault tolerance, and observability in deployed applications Required Skills & Experience8+ years of experience in software engineering, with at least 3+ years in AI/ML systemsExpertise in Python and hands-on experience with RAG pipelines , LLMs (GPT, Claude, LLaMA, etc.) , and embedding modelsGCP stack: Vertex AI, Cloud Functions, Firestore, BigQueryDeep understanding of enterprise integrations including SSO , authentication, data privacy, and complianceExperience with vector databases like Pinecone, FAISS, Weaviate, or Azure Cognitive SearchFamiliarity 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 SkillsExperience with LangChain , LlamaIndex , or similar GenAI orchestration frameworksKnowledge of MLOps practices and tools (e.g., MLflow, Azure DevOps)Familiarity with CI/CD pipelines and containerization using Docker & Kubernetes