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ISACA

AI Solutions Engineer

AI Application EngineeringUpdated on Nov 10
Full-timeRemote in USA$104.2k - $156.3k /yr

About the Job

Requirements

  • A bachelor's degree in Computer Science, Software Engineering, Information Technology, Data Science, Artificial Intelligence, or a related field is required.
  • At least 5 years of professional software or application development experience is required, including hands-on experience developing applications or solutions from 0 to 1 and developing, implementing, or integrating LLM-enabled applications.
  • Experience integrating applications with APIs, enterprise systems, databases, or other data sources.
  • Experience developing or supporting applications in a cloud-based environment.
  • Experience applying security and data protection practices to application development and system integrations.
  • Proficiency in Python and strong programming and application development skills.
  • Working knowledge of artificial intelligence, generative AI, large language models, prompt engineering, retrieval-augmented generation, and agentic workflows.
  • Working knowledge of cloud technologies, AI services, information security, data privacy, and secure development practices.
  • Ability to translate business requirements into practical, secure, and scalable technical solutions.
  • Ability to communicate technical concepts to technical and non-technical audiences.
  • Strong critical thinking, analytical, troubleshooting, and problem-solving skills.
  • Ability to collaborate across functions and manage stakeholders without direct authority.
  • Ability to manage multiple priorities and work independently with initiative, accountability, adaptability, and sound judgment.

Responsibilities

  • Design, develop, test, deploy, and maintain LLM-powered applications, integrations, and agentic AI solutions using Azure cloud technologies, Azure OpenAI, and related AI services.
  • Develop Python-based applications and services for natural language processing, generative AI, and other AI-enabled business use cases.
  • Design and implement agentic workflows that orchestrate AI services, models, tools, and internal or external data sources to address business requirements.
  • Develop and implement retrieval-augmented generation, prompt engineering, semantic search, embeddings, and related techniques for AI application functionality.
  • Develop reusable application components, services, and technical patterns to support AI capabilities across internal applications.
  • Develop prototypes and proofs of concept and transition approved solutions into production applications.
  • Integrate Azure OpenAI APIs, AI services, and external models with internal applications, systems, and data sources.
  • Design and maintain reliable, scalable, performant, and maintainable integrations.
  • Develop and optimize prompts, model configurations, and parameters to improve application performance, response quality, and cost efficiency.
  • Configure and fine-tune models as appropriate to application requirements and available technologies.
  • Monitor, troubleshoot, and debug agentic workflows, application integrations, and AI-generated outputs.
  • Evaluate changes in LLM and AI technologies and recommend modifications or enhancements to existing applications and technical approaches.
  • Develop automated evaluation methods and frameworks to monitor the quality, accuracy, reliability, and performance of AI-enabled applications.
  • Monitor and evaluate AI-generated outputs for hallucinations, performance degradation, and other quality or reliability concerns.
  • Implement technical safeguards against prompt injection, inappropriate or harmful outputs, unauthorized access, and exposure of confidential or sensitive organizational data.
  • Develop AI solutions in accordance with organizational information security, privacy, data governance, and technology requirements.
  • Work with Information Security and other functions to identify and address technical risks associated with AI applications and agentic workflows.
  • Monitor and maintain AI solutions to identify and resolve application, model, integration, and performance issues.
  • Collaborate with business and technology teams to translate business requirements into technical requirements and AI-enabled solutions.
  • Participate in requirements gathering, solution design, technical planning, and implementation.
  • Communicate technical requirements, capabilities, limitations, dependencies, and risks to technical and non-technical stakeholders.
  • Coordinate with Information Technology, Information Security, Data, and other teams on system integrations, data requirements, security controls, and deployment dependencies.
  • Provide technical input on the feasibility and implementation requirements of proposed AI use cases.
  • Create and maintain technical documentation for application code, system architecture, integrations, configurations, agentic workflows, and technical dependencies.
  • Document architecture decisions, technical approaches, development standards, and solution configurations to support ongoing maintenance and team knowledge sharing.
  • Maintain documentation as applications, integrations, and technical requirements change.

Desired Qualifications

  • An advanced degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical field.
  • At least 8 years of professional software or application development experience, including at least 2 years of hands-on experience developing, implementing, or integrating AI, generative AI, LLM-powered, or agentic AI solutions.
  • Experience developing or integrating AI and LLM solutions in a complex or enterprise environment.
  • Experience with Microsoft Azure, Azure OpenAI, Azure AI services, or comparable cloud-based AI platforms.
  • Experience developing agentic AI workflows and orchestrating AI models, tools, APIs, and data sources.
  • Experience with retrieval-augmented generation, prompt engineering, embeddings, semantic search, or vector databases.
  • Experience implementing security controls and safeguards for AI-enabled applications.
  • Experience developing evaluation or monitoring approaches for AI application performance, accuracy, hallucinations, or model drift.
  • Experience optimizing LLM prompts, model configurations, or parameters for performance, reliability, and cost efficiency.
  • Experience working within a complex, enterprise, international, or global organization.

About the Company

Company size
Not listed
Company stage
Not listed
Total funding
Not listed
Headquarters
Schaumburg, IL
Founded
1969

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