Agentic AI Engineer

Responsibilities

• Design, develop, and deploy AI agents to automate complex workflows and business processes.
• Build agentic AI applications using LLMs and Generative AI technologies.
• Develop multi-agent systems that collaborate to solve complex problems.
• Integrate AI agents with external APIs, databases, and enterprise applications.
• Implement tool calling, function calling, memory management, and task planning.
• Develop Retrieval-Augmented Generation (RAG) pipelines for knowledge-based applications.
• Work with frameworks such as LangChain, LangGraph, CrewAI, or AutoGen.
• Design prompt engineering strategies to improve agent performance and response quality.
• Implement guardrails, validation, monitoring, and error-handling mechanisms.
• Evaluate AI agent accuracy, reliability, latency, and cost.
• Optimize agent workflows for scalability, efficiency, and security.
• Collaborate with cross-functional teams to identify automation opportunities.
• Document system architecture, workflows, and technical implementation.

Requirements

• Strong programming knowledge of Python.
• Understanding of Artificial Intelligence, Machine Learning, and Generative AI.
• Knowledge of LLMs, prompt engineering, and transformer-based models.
• Familiarity with AI agent frameworks such as LangChain, LangGraph, CrewAI, or AutoGen.
• Understanding of tool calling, function calling, and agent orchestration.
• Knowledge of RAG, embeddings, vector databases, and semantic search.
• Experience with REST APIs, JSON, and database integration.
• Familiarity with vector databases such as FAISS, Chroma, Pinecone, or Weaviate.
• Understanding of Git, debugging, and software development best practices.
• Strong analytical, problem-solving, and communication skills.

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