AI Integration Engineer (Hybrid)

VIATEQ Corporation•Washington, DC

3d•$130,000 - $160,000•Hybrid

About The Position

VIATEQ Corporation is seeking an AI Integration Engineer to join their team. This hybrid role focuses on deploying, configuring, and optimizing artificial intelligence platforms and tools within an enterprise IT environment. The ideal candidate will have hands-on experience integrating AI solutions, particularly Perplexity AI and other large language models (LLMs), into existing enterprise systems and workflows. You will collaborate with various teams to implement scalable, secure, and mission-aligned AI integration solutions that enhance organizational capabilities and operational efficiency. The position requires the ability to obtain a Public Trust clearance.

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.
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.
Strong troubleshooting and diagnostic skills.
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.
Familiarity with Agile development methodologies.
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.
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.
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.
Familiarity with CI/CD pipeline concepts and DevOps practices.
Experience with enterprise search and knowledge management platforms.
Knowledge of cybersecurity principles and secure coding practices.

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.
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.
Monitor deployed AI integrations for performance, reliability, and output quality, proactively identifying and resolving issues and implementing improvements.
Collaborate with cybersecurity and compliance teams to ensure 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.
Participate in agile development sprints, contributing to backlog grooming, sprint planning, and retrospective activities.
Provide technical guidance and support to other team members on AI platform capabilities, integration patterns, and best practices.
Stay current with the rapidly evolving AI technology landscape, including advancements in LLM capabilities, AI integration frameworks, and enterprise AI deployment methodologies.