Microsoft Artificial Intelligence (AI) Platform
Microsoft Artificial Intelligence (AI) Platform
accenture in the philippines- Posted 3 hours ago
- Be among the first 10 applicants
Job Description
Design, build and configure applications to meet business process and application requirements.
A Microsoft Azure Agentic AI Engineer is responsible for designing and developing autonomous AI agents using orchestration frameworks such as Semantic Kernel and LangChain. This role focuses on building multi-step reasoning systems that can plan, act, and adapt using Azure OpenAI and enterprise tools, enabling intelligent automation and decision support.
Responsibilities
Agentic AI Development
Build intelligent agents using Semantic Kernel, LangChain, or similar orchestration frameworks.
Design workflows that incorporate planning, memory, tool use, and adaptive reasoning.
Implement prompt chaining and function calling to enable complex task execution.
Integration & Prototyping
Integrate agentic AI systems with Azure OpenAI, enterprise APIs, and business applications.
Prototype and validate agentic use cases for automation, decision support, and knowledge management.
Responsible AI & Safety
Apply Responsible AI principles to ensure agent transparency, safety, and ethical behavior.
Monitor agent performance and behavior, ensuring alignment with business and compliance standards.
Qualifications
Hands-on experience with Semantic Kernel, LangChain, or similar agentic frameworks.
Strong background in Azure AI services, including Azure OpenAI and Azure Functions.
Proficient in Python and orchestration logic for multi-step workflows.
Solid understanding of LLMs, prompt chaining, and autonomous agent design.
Excellent analytical, problem-solving, and collaboration skills.
Preferred Skills
Experience with vector search, memory stores, and tool integration for agents.
Familiarity with Microsoft Copilot extensibility and plugin development.
Knowledge of MLOps and observability practices for agentic systems.
Microsoft certifications (e.g., Azure AI Engineer Associate or Semantic Kernel Developer) are a plus.
A Microsoft Azure Agentic AI Engineer is responsible for designing and developing autonomous AI agents using orchestration frameworks such as Semantic Kernel and LangChain. This role focuses on building multi-step reasoning systems that can plan, act, and adapt using Azure OpenAI and enterprise tools, enabling intelligent automation and decision support.
Responsibilities
Agentic AI Development
Build intelligent agents using Semantic Kernel, LangChain, or similar orchestration frameworks.
Design workflows that incorporate planning, memory, tool use, and adaptive reasoning.
Implement prompt chaining and function calling to enable complex task execution.
Integration & Prototyping
Integrate agentic AI systems with Azure OpenAI, enterprise APIs, and business applications.
Prototype and validate agentic use cases for automation, decision support, and knowledge management.
Responsible AI & Safety
Apply Responsible AI principles to ensure agent transparency, safety, and ethical behavior.
Monitor agent performance and behavior, ensuring alignment with business and compliance standards.
Qualifications
Hands-on experience with Semantic Kernel, LangChain, or similar agentic frameworks.
Strong background in Azure AI services, including Azure OpenAI and Azure Functions.
Proficient in Python and orchestration logic for multi-step workflows.
Solid understanding of LLMs, prompt chaining, and autonomous agent design.
Excellent analytical, problem-solving, and collaboration skills.
Preferred Skills
Experience with vector search, memory stores, and tool integration for agents.
Familiarity with Microsoft Copilot extensibility and plugin development.
Knowledge of MLOps and observability practices for agentic systems.
Microsoft certifications (e.g., Azure AI Engineer Associate or Semantic Kernel Developer) are a plus.
More Info
Job Type:
Industry:
Employment Type:
Key Skills
LangChain
Vector search
Microsoft Copilot extensibility
Memory stores
Azure OpenAI
Semantic Kernel
Observability practices
Orchestration frameworks
Prompt chaining
Function calling
Tool integration
