Azure AI Engineer

As an Azure Data ScientistAI Engineer for Agent Development you will be at the forefront of our AI initiatives transforming complex business problems into sophisticated agentbased solutions You will work closely with crossfunctional teams to understand requirements design agent architectures implement and optimize agentic behaviors and ensure seamless integration and deployment on Azure

Key Responsibilities

• Agent System Design Architecture Design develop and implement endtoend agentbased AI systems using frameworks such as Autogen Langgraph and CrewAI This includes defining agent roles communication protocols and task orchestration
• Azure Platform Expertise Leverage a wide range of Azure services including Azure Machine Learning Azure OpenAI Service Azure Databricks Azure Functions and other relevant data and AI services for building training deploying and managing agent solutions
• Large Language Model LLM Integration Integrate and finetune LLMs eg GPTx models via Azure OpenAI within agentic workflows focusing on prompt engineering context management and optimizing LLM interactions for specific tasks
• Data Preparation Feature Engineering Work with diverse datasets on Azure performing data cleaning transformation and feature engineering to prepare data for agent consumption and to enhance agent performance
• Workflow Orchestration Develop and manage complex stateful agent workflows using Langgraph for intricate decisionmaking processes loops and conditional logic
• MultiAgent Collaboration Implement and optimize multiagent collaboration strategies using Autogen to enable agents to converse delegate tasks and collectively solve problems
• CrewAI Implementation Utilize CrewAI to define and orchestrate collaborative agent crews with distinct roles tools and shared objectives for streamlined task execution
• Tool Development Develop custom tools and integrations for agents to interact with external APIs databases and other systems extending their capabilities
• Deployment MLOps Operationalize agent solutions on Azure including containerization Docker KubernetesAKS setting up CICD pipelines monitoring agent performance and implementing robust MLOps practices for continuous improvement
• Performance Optimization Scalability Identify and implement strategies to optimize the performance efficiency and scalability of agent systems on Azure
• Research Innovation Stay uptodate with the latest advancements in AI LLMs and agentic AI frameworks researching and evaluating new technologies for potential application
• Collaboration Communication Work effectively with data engineers software engineers product managers and business stakeholders to translate requirements into technical solutions and communicate complex concepts clearly
• Responsible AI Ensure the ethical development and deployment of AI agents addressing bias fairness transparency and security concerns

Required Skills and Qualifications

• Bachelors or Masters degree in Computer Science Data Science Artificial Intelligence or a related quantitative field If PhD will be good
• Proven experience 12yrs as a Data Scientist or AI Engineer with a strong focus on building and deploying machine learning or AI solutions
• Deep expertise in Azure cloud services specifically Azure Machine Learning Azure OpenAI Service Azure Databricks Azure Functions Azure Data Lake Storage etc
• Strong programming skills in Python Python PyTorch TensorFlow Scikitlearn
• Handson experience with agent orchestration frameworks
• Autogen Experience in building multiagent conversational systems and enabling dynamic collaboration
• Langgraph Proficiency in designing and implementing stateful graphbased agent workflows with complex logic
• CrewAI Experience in orchestrating roleplaying autonomous AI agents for collaborative task execution
• Solid understanding of Large Language Models LLMs prompt engineering and RAG Retrieval Augmented Generation techniques
• Experience with MLOps practices including CICD model monitoring and version control Git
• Familiarity with containerization technologies Docker Kubernetes
• Strong analytical and problemsolving skills with the ability to translate business requirements into technical solutions
• Excellent communication and collaboration skills