Job Description
Responsibilities
• Assist in designing and implementing agentic architectures using LLMs and reasoning engines.
• Contribute to the development of multi-agent systems for task orchestration.
• Integrate external tools, REST APIs, and knowledge bases to augment agent capabilities.
• Write clean, well-documented Python code for agent logic and workflow components.
• Support testing, evaluation, and debugging of agent pipelines.
• Research agentic frameworks and prototype capabilities such as memory and planning modules.
• Collaborate with cross-functional teams to embed agentic AI into business applications.
Additional Responsibilities: Good to Have
• Exposure to RAG (Retrieval-Augmented Generation) pipelines.
• Familiarity with vector databases (Pinecone, Weaviate, Chroma).
• Basic knowledge of Docker and cloud environments (AWS, Azure, or GCP)
Technical and Professional Requirements:
• Solid Python programming with experience in AI/ML frameworks (PyTorch, TensorFlow, or Scikit-learn).
• Foundational understanding of LLMs and prompt engineering techniques.
• Exposure to at least one agentic framework: LangChain, LangGraph, AutoGen, or CrewAI.
• Basic knowledge of REST API integration and tool augmentation for agents.
• Familiarity with responsible AI principles and safety considerations in LLM systems
Preferred Skills:
Technology->OpenSystem->Python – OpenSystem->Python
Technology->AI-AI Hyperscalers->Azure Agentic AI Services
Technology->AI-AI Hyperscalers->Google Agentic AI Services
Technology->AI-Agentic AI->Google Cloud – Contact Center AI
Technology->AI-AI Hyperscalers->AWS Agentic AI Services
Technology->AI-Agentic AI->Agent Engineering
Technology->AI-Agentic AI->AgentOps
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