Generative AI Specialist

Design, develop, and deploy Generative AI applications using Large Language Models (LLMs). Build and optimize Retrieval-Augmented Generation (RAG) pipelines to enable accurate, context-aware responses based on enterprise data. Develop and implement AI agents and multi-agent systems using Agentic AI frameworks. Integrate LLMs with internal systems, APIs, databases, and external tools. Implement prompt engineering techniques, context management, and model fine-tuning when required. Work with vector databases and embedding models to improve information retrieval and semantic search. Develop scalable AI solutions using frameworks such as LangChain, LangGraph, and LlamaIndex. Evaluate and optimize model performance, accuracy, latency, and cost. Implement security, access control, data privacy, and responsible AI practices. Collaborate with software engineers, data engineers, and business stakeholders to translate business requirements into AI-driven solutions. Deploy, monitor, and maintain AI applications in production environments. Requirements Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Computer Engineering, or a related field. 1-5 years of experience in AI/ML, Software Engineering, or a related technical field, with practical experience in Generative AI development. Strong programming skills in Python. Hands-on experience working with Large Language Models (LLMs), such as GPT, Claude, Gemini, or open-source models (Llama, Mistral). Solid experience designing and implementing Retrieval-Augmented Generation (RAG) architectures. Practical experience with embeddings, semantic search, vector databases, and document processing. Hands-on experience developing AI agents and agentic workflows. Familiarity with frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or CrewAI. Experience integrating AI models through REST APIs and working with backend technologies. Understanding of prompt engineering, context windows, tokenization, and model evaluation techniques. Knowledge of cloud platforms such as AWS, Microsoft Azure, or Google Cloud. Familiarity with Docker, Git, CI/CD, and deploying AI applications is preferred. Preferred Qualifications: Experience with LLM fine-tuning, PEFT, LoRA, or quantization. Experience implementing advanced RAG techniques, including hybrid search, reranking, and query expansion. Knowledge of AI safety, guardrails, hallucination mitigation, and LLM observability. Experience building production-grade multi-agent systems and autonomous workflows. Familiarity with MLOps or LLMOps practices.