Lead Developer - GenAI & RAG Systems

Job Title: 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