Agentic AI Engineer

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