Artificial Intelligence Engineer

Job Scope/ Requirements

• Develop and enhance AI-powered applications using LLMs and Generative AI technologies.
• Design, implement, and optimize RAG pipelines for intelligent knowledge retrieval and question-answering systems.
• Build and integrate AI agents capable of autonomous reasoning, planning, and task execution.
• Develop and integrate MCP tools to connect AI agents with enterprise systems, databases, and engineering services.
• Assist in evaluating, fine-tuning, and benchmarking LLMs for specific business use cases.
• maintain prompt engineering strategies to improve AI response quality and reliability.
• Work with vector databases, embeddings, and semantic search technologies.
• Conduct testing, debugging, and model performance analysis to ensure AI solution quality.
• Document technical designs, implementation guides, and AI solution architectures.
• Support deployment and maintenance of AI services in cloud and enterprise environments.
• Involve in all the stages of development from architecting to implementing
• Skillset -Bachelor's degree (or pursuing final year) in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or related fields.
• Basic understanding of LLMs and Generative AI concepts.
• Familiarity with programming languages such as Python.
• Knowledge of GitHub and software development lifecycle practices.
• Understanding of REST APIs and system integration concepts.
• Familiarity with prompt engineering and AI application development.
• Strong problem-solving and analytical skills.
• Eagerness to learn emerging AI technologies and frameworks.

Preferred Skills

• Experience with OpenAI, Azure OpenAI, Claude, Gemini, or open-source LLMs.
• Knowledge of Agentic AI frameworks (LangGraph, LangChain, CrewAI, AutoGen, etc.).
• Familiarity with RAG, vector databases, embeddings, and semantic search.
• Understanding of MCP (Model Context Protocol) concepts and tool integrations.
• Experience with FastAPI, Python backend development, or cloud services.
• Exposure to AI evaluation, model benchmarking, or LLMOps practices