AI Integration Engineer

Koniag Government Services, LLC•Washington, DC

12d•$130,000 - $160,000•Hybrid

About The Position

Koniag IT Systems, LLC a Koniag Government Services company, is seeking an AI Integration Engineer to support KITS and our government customer Washington, DC. The position is hybrid, requires 1 day onsite. This position requires the candidate to be able to obtain a Public Trust. Koniag IT Systems, a Koniag Government Services company, is seeking a technically skilled and innovative AI Integration Engineer to support the deployment, configuration, and ongoing optimization of artificial intelligence platforms and tools within an enterprise IT environment. The ideal candidate is a hands-on engineer with demonstrated experience integrating AI-powered solutions — including Perplexity AI and related large language model (LLM) technologies — into existing enterprise systems, workflows, and applications. This individual will work closely with the AI Integration Lead, IT architects, development teams, and business stakeholders to implement scalable, secure, and mission-aligned AI integration solutions that drive operational efficiency and enhance organizational capabilities. The AI Integration Engineer will serve as a hands-on technical contributor responsible for the implementation, configuration, integration, and maintenance of AI platforms and tools — with a focus on Perplexity AI and enterprise LLM technologies — within the client's IT environment. This individual will engineer the connections between AI platforms and enterprise systems, develop and refine integration pipelines, support the operationalization of AI use cases identified by the AI Integration Lead, and ensure that deployed AI solutions are performant, secure, and aligned with organizational and federal compliance requirements.

Requirements

Bachelor's degree in Computer Science, Information Technology, Software Engineering, Data Science, or a related field from an accredited college or university, or equivalent work experience.
3+ years of experience in systems integration, software development, API engineering, or a related technical discipline.
1+ years of hands-on experience deploying, configuring, or integrating AI platforms, large language models, or generative AI tools within an enterprise environment.
Demonstrated experience developing and consuming REST APIs and building integrations between enterprise systems using APIs, SDKs, webhooks, or middleware platforms.
Experience with at least one major cloud platform (Microsoft Azure, AWS, or Google Cloud Platform) and its native AI and integration service offerings.
Exceptional communication skills in English – both written and oral – with the ability to clearly document and communicate technical integration designs, findings, and recommendations to both technical and non-technical audiences.
Hands-on experience with Perplexity AI, OpenAI API, Microsoft Azure OpenAI Service, Google Vertex AI, or equivalent enterprise-grade AI and LLM platforms, including API configuration, authentication, and integration development.
Proficiency in one or more programming or scripting languages relevant to AI integration development, including Python, JavaScript, PowerShell, or equivalent, with demonstrated experience developing API integrations, automation scripts, and data processing pipelines.
Strong understanding of REST API design and consumption principles, including experience with authentication mechanisms such as OAuth2, API keys, and token-based access management.
Familiarity with retrieval-augmented generation (RAG) architectures and experience configuring or working with vector databases and embedding technologies such as Pinecone, Weaviate, Azure AI Search, Chroma, or equivalent platforms.
Experience integrating AI platforms with enterprise applications and collaboration tools, including Microsoft 365, SharePoint, ServiceNow, Microsoft Teams, or equivalent enterprise systems.
Knowledge of cloud platform services relevant to AI integration, including Azure AI Services, AWS Bedrock, or Google Cloud AI Platform, and their native integration and orchestration capabilities.
Familiarity with prompt engineering principles and techniques, including the design and optimization of system prompts, few-shot examples, and context management strategies to improve AI output quality and reliability.
Strong troubleshooting and diagnostic skills with the ability to identify, isolate, and resolve issues across complex AI integration architectures and multi-system environments.
Experience conducting testing activities for AI integration solutions, including functional, performance, and output quality evaluation.
Ability to develop and maintain clear and comprehensive technical documentation, including integration architecture diagrams, API guides, data flow documentation, and operational runbooks.
Familiarity with Agile development methodologies and the ability to contribute effectively within iterative development frameworks.
Ability to obtain a Public Trust Clearance.

Nice To Haves

Experience working with Perplexity AI, OpenAI, Azure OpenAI Service, or equivalent enterprise LLM platforms in a hands-on integration or deployment capacity.
Familiarity with retrieval-augmented generation (RAG) architectures and vector database technologies.
Experience supporting federal government IT environments and familiarity with FedRAMP-authorized AI platforms.
Direct hands-on experience with Perplexity AI platform APIs, enterprise deployment configurations, and integration with organizational knowledge bases and data sources.
Experience working in a federal government IT environment, including familiarity with FedRAMP, FISMA, NIST, and related compliance and security frameworks governing AI platform deployments.
Familiarity with AI orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent tools used to coordinate multi-step LLM workflows and RAG pipelines.
Experience with Microsoft 365 Copilot, Power Platform AI capabilities, or SharePoint-based AI integrations within enterprise Microsoft environments.
Knowledge of data governance and responsible AI principles, including data privacy, bias mitigation, explainability, and output validation practices relevant to enterprise AI deployments.
Google Professional Machine Learning Engineer, Microsoft Azure AI Engineer Associate (AI-102), AWS Certified Machine Learning – Specialty, or equivalent AI/ML platform certification.
Experience with containerization and deployment technologies such as Docker and Kubernetes as they relate to AI application and integration solution deployment.
Familiarity with CI/CD pipeline concepts and DevOps practices as applied to AI integration solution development, testing, and deployment workflows.
Experience with enterprise search and knowledge management platforms and their integration with AI-powered retrieval and question-answering capabilities.
Knowledge of cybersecurity principles and secure coding practices as they apply to AI platform integrations, including input validation, output sanitization, and protection against prompt injection and other AI-specific security risks.

Responsibilities

Design, develop, and implement technical integrations between AI platforms — including Perplexity AI and other LLM or generative AI tools — and existing enterprise systems, applications, data sources, and workflows using REST APIs, SDKs, webhooks, and middleware solutions.
Configure and deploy Perplexity AI and related AI platform environments, including API connectivity, authentication, access controls, and integration with enterprise data sources and knowledge repositories.
Develop and maintain integration pipelines, data flows, and automation scripts that connect AI tools with enterprise applications such as SharePoint, Microsoft 365, ServiceNow, collaboration platforms, and other organizational systems.
Implement and refine retrieval-augmented generation (RAG) architectures, including the configuration of vector databases, document ingestion pipelines, embedding workflows, and AI search integrations to enhance the accuracy and relevance of AI-generated outputs.
Collaborate with the AI Integration Lead and business stakeholders to translate identified AI use cases into technically sound, scalable, and maintainable integration solutions.
Support the configuration and deployment of AI-powered self-service capabilities, including intelligent search, virtual assistants, knowledge retrieval tools, and workflow automation integrations.
Conduct thorough testing of AI integration solutions, including functional testing, performance testing, and output quality evaluation, ensuring that deployed solutions meet defined acceptance criteria and operational standards prior to production release.
Monitor deployed AI integrations for performance, reliability, and output quality, proactively identifying and resolving issues and implementing improvements to optimize solution effectiveness.
Collaborate with cybersecurity and compliance teams to ensure that AI platform integrations adhere to organizational data handling policies, access control requirements, and federal security and compliance frameworks.
Maintain accurate and comprehensive technical documentation for all AI integration solutions, including architecture diagrams, API integration guides, data flow documentation, configuration records, and operational runbooks.
Support the evaluation and proof-of-concept development for emerging AI tools and platforms, providing technical assessments and recommendations to the AI Integration Lead and IT leadership.
Participate in Agile development sprints, contributing to backlog grooming, sprint planning, and retrospective activities related to AI integration initiatives.
Provide technical guidance and support to other team members on AI platform capabilities, integration patterns, and best practices for enterprise AI deployment.
Stay current with the rapidly evolving AI technology landscape, including advancements in LLM capabilities, AI integration frameworks, and enterprise AI deployment methodologies.