Role | Python Engineer LLMBased AI Agents |
Visit our websiteto know more. Follow us onLinkedInIInstagramIFacebookIXfor the exciting updates.
|
About the UNIT/ Unit Overview | 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 |
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Experience: |
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Number of openings
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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/Controller Executor/Worker Scratchpads 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.
- Define clear agent roles (Supervisor Planner Executor).
- Implement agent communication protocols (A2A MCP).
- Enable coordination negotiation and joint problemsolving between agents.
MLOps & Deployment - Deploy agent services in Kubernetes and manage containerized workloads.
- Build CI/CD pipelines using GitHub/Jenkins for agent services prompts and toolchains.
- Monitor performance metrics like latency token efficiency retry rate error rate and hallucination rate.
Agent Evaluation & Quality Assurance Apply modern evaluation frameworks and metrics: - Use DeepEval LangChain Testing Utilities TruLens Ragas Promptfoo and Guardrails for automated testing.
- Evaluate correctness relevance groundedness consistency SQL validity (for SQL agents) and semantic accuracy.
- Build automated regression test suites for prompts models and agent orchestration.
- Measure SQL metrics (Query Validity Semantic Correctness Filter Accuracy Overquerying Aggregation Quality).
Delivery & Support model: | - Standard Business Hours 5x9 (Mo - Fr 08:00 17:00 (CET/CEST DST included).
- Ops activities in standard business hours during India public holidays at least one colleague must be on call.
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What should you bring along
| Expected skill Sets and experience that the candidates should bring along. Key Qualifications and Skills: - Expert-level Python skills (async OOP microservices).
- Strong experience with LLMs and agent frameworks:
- LangChain LlamaIndex Haystack CrewAI AutoGen DSPy.
- Deep knowledge of agent concepts:
- Planner Executor Memory Reflection ReAct Scratchpads Prompt Chaining.
- Experience with RAG vector databases embeddings semantic search.
- Experience with FastAPI Docker Kubernetes GitHub CI/CD.
- Understanding of prompting techniques:
- ZeroShot / FewShot / OneShot CoT Dynamic Prompting SelfAsk.
- Knowledge of guardrails safety evaluation and reliability engineering.
Additional Skills: - Experience in automotive processes or enterprise-grade data systems.
- Knowledge of monitoring tools (Prometheus Grafana ELK).
- Familiarity with model registries (MLflow) or agent evaluation stacks.
- Understanding of world models semantic memory and context compression.
- Experience tuning or optimizing open source models (LLaMA Qwen Mistral).
|
Must have technical skill | - Python (advanced)
- LLMs & Prompting
- ReAct CoT Scratchpads Planner/Executor patterns
- LangChain / LlamaIndex / Haystack
- RAG Embeddings Vectorstores
- FastAPI
- Docker
- Kubernetes
- GitHub / CI/CD
|
Good to have technical skills | - CrewAI AutoGen DSPy
- Azure AI services
- Logging & Observability
- Testing frameworks: DeepEval Ragas Promptfoo Guardrails
- Multi agent orchestration patterns
|
Required Experience:
Manager
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...
Role | Python Engineer LLMBased AI Agents |
Visit our websiteto know more. Follow us onLinkedInIInstagramIFacebookIXfor the exciting updates.
|
About the UNIT/ Unit Overview | 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: |
|
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/Controller Executor/Worker Scratchpads 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.
- Define clear agent roles (Supervisor Planner Executor).
- Implement agent communication protocols (A2A MCP).
- Enable coordination negotiation and joint problemsolving between agents.
MLOps & Deployment - Deploy agent services in Kubernetes and manage containerized workloads.
- Build CI/CD pipelines using GitHub/Jenkins for agent services prompts and toolchains.
- Monitor performance metrics like latency token efficiency retry rate error rate and hallucination rate.
Agent Evaluation & Quality Assurance Apply modern evaluation frameworks and metrics: - Use DeepEval LangChain Testing Utilities TruLens Ragas Promptfoo and Guardrails for automated testing.
- Evaluate correctness relevance groundedness consistency SQL validity (for SQL agents) and semantic accuracy.
- Build automated regression test suites for prompts models and agent orchestration.
- Measure SQL metrics (Query Validity Semantic Correctness Filter Accuracy Overquerying Aggregation Quality).
Delivery & Support model: | - Standard Business Hours 5x9 (Mo - Fr 08:00 17:00 (CET/CEST DST included).
- Ops activities in standard business hours during India public holidays at least one colleague must be on call.
|
|
What should you bring along
| Expected skill Sets and experience that the candidates should bring along. Key Qualifications and Skills: - Expert-level Python skills (async OOP microservices).
- Strong experience with LLMs and agent frameworks:
- LangChain LlamaIndex Haystack CrewAI AutoGen DSPy.
- Deep knowledge of agent concepts:
- Planner Executor Memory Reflection ReAct Scratchpads Prompt Chaining.
- Experience with RAG vector databases embeddings semantic search.
- Experience with FastAPI Docker Kubernetes GitHub CI/CD.
- Understanding of prompting techniques:
- ZeroShot / FewShot / OneShot CoT Dynamic Prompting SelfAsk.
- Knowledge of guardrails safety evaluation and reliability engineering.
Additional Skills: - Experience in automotive processes or enterprise-grade data systems.
- Knowledge of monitoring tools (Prometheus Grafana ELK).
- Familiarity with model registries (MLflow) or agent evaluation stacks.
- Understanding of world models semantic memory and context compression.
- Experience tuning or optimizing open source models (LLaMA Qwen Mistral).
|
Must have technical skill | - Python (advanced)
- LLMs & Prompting
- ReAct CoT Scratchpads Planner/Executor patterns
- LangChain / LlamaIndex / Haystack
- RAG Embeddings Vectorstores
- FastAPI
- Docker
- Kubernetes
- GitHub / CI/CD
|
Good to have technical skills | - CrewAI AutoGen DSPy
- Azure AI services
- Logging & Observability
- Testing frameworks: DeepEval Ragas Promptfoo Guardrails
- Multi agent orchestration patterns
|
Required Experience:
Manager
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