Job
Description
As an AI Agentic Architect/Lead, your role involves taking charge of the end-to-end vision, design, and successful deployment of complex, autonomous AI systems (Agents). Your expertise in architecting and deploying AI/ML solutions on leading cloud platforms such as Azure/AWS/GCP will be crucial. Additionally, you should be proficient in Python and modern ML/AI frameworks to design, implement, and optimize intricate agentic workflows. **Key Responsibilities:** - Lead the design and development of agentic systems using GenAI and Machine Learning, selecting appropriate frameworks and architectural patterns like LangChain, LlamaIndex, or proprietary solutions. - Engineer dynamic context through memory modules, prompt templates, and retrieval strategies while enforcing guardrails and context governance across agent interactions and LLM outputs to prioritize security. - Own the full lifecycle of data sourcing, model development, orchestration, deployment, and LLMOps, rapidly prototyping solutions and transitioning MVPs into robust, scalable enterprise applications meeting core KPIs. - Demonstrate hands-on experience with agentic AI frameworks like LangChain, LangGraph, CrewAI, or equivalent, designing multi-agent workflows and tool-augmented reasoning systems. - Apply advanced model selection, feature engineering, and evaluation techniques, and establish monitoring strategies using cloud-native tools such as Azure Monitor and Application Insights. - Implement MLOps/LLMOps practices for tracking model iterations, deployment gates, rapid restoration, and monitoring drift detection, and design automated CI/CD pipelines with tools like Azure DevOps, GitHub Actions, etc. **Qualifications Required:** - Extensive experience in architecting and deploying AI/ML solutions on cloud platforms like Azure/AWS/GCP - Proficiency in Python and modern ML/AI frameworks - Hands-on expertise in agentic AI frameworks such as LangChain, LangGraph, CrewAI, or equivalent - Strong knowledge of model selection, feature engineering, and evaluation techniques - Experience in setting up monitoring strategies using cloud-native tools - Familiarity with MLOps/LLMOps practices and CI/CD pipelines Please note that the company details were not provided in the job description. As an AI Agentic Architect/Lead, your role involves taking charge of the end-to-end vision, design, and successful deployment of complex, autonomous AI systems (Agents). Your expertise in architecting and deploying AI/ML solutions on leading cloud platforms such as Azure/AWS/GCP will be crucial. Additionally, you should be proficient in Python and modern ML/AI frameworks to design, implement, and optimize intricate agentic workflows. **Key Responsibilities:** - Lead the design and development of agentic systems using GenAI and Machine Learning, selecting appropriate frameworks and architectural patterns like LangChain, LlamaIndex, or proprietary solutions. - Engineer dynamic context through memory modules, prompt templates, and retrieval strategies while enforcing guardrails and context governance across agent interactions and LLM outputs to prioritize security. - Own the full lifecycle of data sourcing, model development, orchestration, deployment, and LLMOps, rapidly prototyping solutions and transitioning MVPs into robust, scalable enterprise applications meeting core KPIs. - Demonstrate hands-on experience with agentic AI frameworks like LangChain, LangGraph, CrewAI, or equivalent, designing multi-agent workflows and tool-augmented reasoning systems. - Apply advanced model selection, feature engineering, and evaluation techniques, and establish monitoring strategies using cloud-native tools such as Azure Monitor and Application Insights. - Implement MLOps/LLMOps practices for tracking model iterations, deployment gates, rapid restoration, and monitoring drift detection, and design automated CI/CD pipelines with tools like Azure DevOps, GitHub Actions, etc. **Qualifications Required:** - Extensive experience in architecting and deploying AI/ML solutions on cloud platforms like Azure/AWS/GCP - Proficiency in Python and modern ML/AI frameworks - Hands-on expertise in agentic AI frameworks such as LangChain, LangGraph, CrewAI, or equivalent - Strong knowledge of model selection, feature engineering, and evaluation techniques - Experience in setting up monitoring strategies using cloud-native tools - Familiarity with MLOps/LLMOps practices and CI/CD pipelines Please note that the company details were not provided in the job description.
