This role supports DECO in designing developing and operationalizing LLMbased intelligent agents and multiagent systems. You will build autonomous toolusing agents that combine reasoning planning memory and retrieval to automate enterprise processes endtoend across quality logistics production and business domains.
Location
Experience:
Experience in Python machine learning generative AI or backend engineering.
Number of openings
What awaits you/ Job Profile
AI Agent Engineering & LLM Development
JD Description
Key Responsibilities
You will build nextgeneration AI agents using advanced agentic concepts:
Develop LLMbased agents with capabilities such as reasoning planning reflection tool use and memory.
Implement ReAct (Reasoning Acting) patterns and ChainofThought (CoT) prompting for reliable multistep reasoning.
Build agent architectures including Planner/ControllerExecutor/WorkerScratchpads and Short/LongTerm Memory.
Develop ToolWrappers that allow agents to interact with APIs databases ERP systems file systems and search tools.
Build and optimize dynamic prompting pipelines contextual injection prompt chaining and dynamic fewshot selection.
Create selfhealing agents with reflection loops retry logic and errorcorrection strategies.
Implement Guardrails to ensure safety policy compliance and output validation.
Python Engineering
Design and implement scalable backend services (FastAPI/Flask) powering agent pipelines.
Build modular reusable agent components in Python for planning memory tool execution and reasoning control.
Integrate agent services into enterprise IT systems and microservice ecosystems.
RAG Data & Integration
Build RetrievalAugmented Generation (RAG) systems using embeddings and semantic search.
Work with vectorstores such as FAISS Chroma Milvus or Pinecone.
Implement semantic memory context compression and document indexing.
Integrate structured and unstructured data (SQL JSON XML logs domain models KafKa).
MultiAgent System Development
Build multiagent systems (MAS) where planners delegate tasks to worker agents.
RolePython Engineer LLMBased AI AgentsVisit our websiteto know more.Follow us onLinkedInIInstagramIFacebookIXfor the exciting updates.About the UNIT/ Unit OverviewThis role supports DECO in designing developing and operationalizing LLMbased intelligent agents and multiagent systems.You will build a...
This role supports DECO in designing developing and operationalizing LLMbased intelligent agents and multiagent systems. You will build autonomous toolusing agents that combine reasoning planning memory and retrieval to automate enterprise processes endtoend across quality logistics production and business domains.
Location
Experience:
Experience in Python machine learning generative AI or backend engineering.
Number of openings
What awaits you/ Job Profile
AI Agent Engineering & LLM Development
JD Description
Key Responsibilities
You will build nextgeneration AI agents using advanced agentic concepts:
Develop LLMbased agents with capabilities such as reasoning planning reflection tool use and memory.
Implement ReAct (Reasoning Acting) patterns and ChainofThought (CoT) prompting for reliable multistep reasoning.
Build agent architectures including Planner/ControllerExecutor/WorkerScratchpads and Short/LongTerm Memory.
Develop ToolWrappers that allow agents to interact with APIs databases ERP systems file systems and search tools.
Build and optimize dynamic prompting pipelines contextual injection prompt chaining and dynamic fewshot selection.
Create selfhealing agents with reflection loops retry logic and errorcorrection strategies.
Implement Guardrails to ensure safety policy compliance and output validation.
Python Engineering
Design and implement scalable backend services (FastAPI/Flask) powering agent pipelines.
Build modular reusable agent components in Python for planning memory tool execution and reasoning control.
Integrate agent services into enterprise IT systems and microservice ecosystems.
RAG Data & Integration
Build RetrievalAugmented Generation (RAG) systems using embeddings and semantic search.
Work with vectorstores such as FAISS Chroma Milvus or Pinecone.
Implement semantic memory context compression and document indexing.
Integrate structured and unstructured data (SQL JSON XML logs domain models KafKa).
MultiAgent System Development
Build multiagent systems (MAS) where planners delegate tasks to worker agents.
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