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Artificial Intelligence Engineer

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Onsite

Artificial Intelligence Engineer

Kavaliro

Artificial Intelligence Engineer

Skills

Google Cloud PlatformSQL QueriesDockerTypescriptPythonData ScienceC#MicroservicesMachine LearningAIAWSKubernetesNo-code AutomationsCRMSalesforceWorkdaySAP ERPServiceNowMicrosoft TeamsMicrosoft Power PlatformComplianceDigital TransformationJavascriptAzure

7 days ago

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Important information


Contract type:

Permanent

Location:

United States

Work mode:

Onsite

Published on:

15 October 2026

What they need


We are seeking an innovative Agentic AI Engineer to design, develop, and deploy autonomous AI systems that can reason, plan, execute tasks, and collaborate with users and enterprise applications. This role will focus on building AI agents powered by Large Language Models (LLMs), integrating them with business systems, and creating intelligent workflows that drive operational efficiency and business value.

The ideal candidate has experience with AI/ML technologies, modern software development practices, cloud platforms, and agent frameworks such as Microsoft Copilot Studio, Azure AI Foundry, LangChain, AutoGen, CrewAI, or similar technologies.

Key Responsibilities

  • Design and develop autonomous AI agents that perform multi-step reasoning and task execution.
  • Build AI-driven workflows that integrate with enterprise applications, APIs, databases, CRM, ERP, and productivity platforms.
  • Develop Retrieval-Augmented Generation (RAG) solutions using structured and unstructured data sources.
  • Engineer prompt strategies, memory management, planning, and tool-use capabilities for AI agents.
  • Integrate AI agents with Microsoft 365, Azure services, Teams, SharePoint, Dynamics 365, and third-party applications.
  • Create evaluation frameworks to measure agent performance, accuracy, reliability, and business impact.
  • Develop secure and scalable AI solutions following governance, compliance, and security standards.
  • Collaborate with business stakeholders to identify opportunities for AI automation and digital transformation.
  • Monitor AI system performance and continually optimize models, prompts, workflows, and integrations.
  • Stay current with emerging trends in generative AI, agentic AI, LLMs, and enterprise AI platforms.

Required Qualifications

  • Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or related field.
  • 3+ years of software engineering experience.
  • 2+ years developing AI/ML, Generative AI, or intelligent automation solutions.
  • Experience with Python, C#, JavaScript, or other modern programming languages.
  • Experience working with REST APIs, microservices, and cloud-based architectures.
  • Strong understanding of:
  • Large Language Models (LLMs)
  • Prompt Engineering
  • Retrieval-Augmented Generation (RAG)
  • AI Agents and Multi-Agent Systems
  • Vector Databases
  • Model Evaluation and Observability
  • Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud.
  • Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

  • Experience with Azure OpenAI Service and Azure AI Foundry.
  • Experience with Microsoft Copilot Studio or Power Platform.
  • Hands-on knowledge of LangChain, Semantic Kernel, AutoGen, CrewAI, LlamaIndex, or similar frameworks.
  • Familiarity with MLOps, CI/CD pipelines, and DevOps practices.
  • Experience integrating AI solutions with enterprise systems such as Dynamics 365, Salesforce, SAP, ServiceNow, or Workday.
  • Knowledge of AI governance, responsible AI practices, and security frameworks.

Technical Skills

Programming Languages

  • Python
  • C#
  • JavaScript / TypeScript
  • SQL

AI & Machine Learning

  • LLMs (GPT, Claude, Gemini, Llama)
  • Prompt Engineering
  • RAG Architecture
  • Vector Databases (Pinecone, Weaviate, Chroma, Azure AI Search)
  • Agent Frameworks
  • AI Evaluation and Testing

Cloud & Platforms

  • Microsoft Azure
  • Azure OpenAI
  • Azure AI Foundry
  • Microsoft Copilot Studio
  • Azure Functions
  • Docker
  • Kubernetes

Success Metrics

  • Deploy autonomous AI solutions that improve operational efficiency and user productivity.
  • Reduce manual effort through intelligent workflow automation.
  • Increase adoption of AI solutions across business functions.
  • Maintain high-performance, secure, and scalable AI systems.
  • Deliver measurable ROI from generative AI and agent-based initiatives.

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