LLM (ChatGPT) Application Engineer

✔️ Design and develop end-to-end LLM-powered applications using GPT-4, GPT-5 or custom models.

✔️ Integrate ChatGPT with backend systems through REST APIs, WebSockets, and cloud functions.

✔️ Build scalable AI pipelines for summarization, content generation, Q&A, and automation tasks.

✔️ Fine-tune models using available datasets to improve accuracy for specific business domains.

✔️ Implement guardrails, moderation rules, and safety filters to control model output.

✔️ Build vector search systems using FAISS, Pinecone, Weaviate, or Milvus.

✔️ Work with RAG (Retrieval Augmented Generation) frameworks for grounded responses.

✔️ Implement caching, batching, and optimization for cost reduction and performance.

✔️ Develop structured workflows for approvals, escalations, and error handling in LLM interactions.

✔️ Monitor LLM performance through logs, metrics, and analytics dashboards.

✔️ Test model outputs for correctness, consistency, and hallucination risks.

✔️ Collaborate with product and design teams to create LLM-driven UX flows.

✔️ Maintain secure handling of sensitive data used in model queries.

✔️ Implement multi-agent systems for complex AI interactions and decision-making.

✔️ Automate repetitive business tasks using ChatGPT-powered agents.

✔️ Document data flows, architecture, model behaviors, and system dependencies.

✔️ Troubleshoot model-related issues including context loss and misclassification.

✔️ Scale LLM applications using Kubernetes, serverless functions, and cloud orchestration.

✔️ Stay updated with the latest GPT model releases, research papers, and API features.

✔️ Provide internal training on LLM capabilities, limitations, and best practices.