Agentic AI Engineer

• 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.