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.