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Mid-level, full-stack AI role in metro with broad LLM and web skill requirements increases competition.
ML/LLM integration focus with full-stack requirements yields moderate cross-industry transferability.
Explicit 5+ years plus mandatory full-stack and LLM/ML integration skills make screening stringent.
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Design and implement scalable full-stack architectures for AI-driven products, including front-end interfaces and back-end microservices.
Integrate and optimize Large Language Models and machine learning models as core components, ensuring low latency, reliability, and cost efficiency.
Lead development of Retrieval-Augmented Generation (RAG) systems, including data chunking, embedding, indexing, and retrieval for contextual AI responses.
5+ years of experience as a Full Stack Software Engineer, with at least 1 year of AI/ML integration experience.
Strong front-end development skills in TypeScript/JavaScript with frameworks like React or Next.js.
Advanced back-end development expertise in Python (preferred) or Node.js, including scalable API development.
Proficiency with LLM APIs (OpenAI, Gemini, Claude), frameworks like LangChain or LlamaIndex, and experience with containerization (Docker) and cloud deployments (AWS, GCP).
Experienced full-stack engineer comfortable bridging data science and product engineering to build intelligent user-facing AI features.
Hands-on expertise in productizing AI models and managing AI/ML lifecycle in production including performance optimization and deployment.
Familiarity with advanced AI infrastructure such as RAG systems, vector databases, MLOps practices, and cloud orchestration tools like Kubernetes.