Role

We are seeking an AI Specialist who is an engineer first. You will join our technical team to embed automated reasoning directly into our infrastructure footprints and optimize developer velocity. You must have the seasoned instinct to know exactly where AI adds transformative value and where simple, deterministic, or traditional algorithmic logic is superior. Your job is to prototype highly secure, often offline-first AI integrations rapidly and refine them into robust, production-grade internal and external enterprise systems within tight deadlines.

Key Responsibilities

Accelerate the Engineering Lifecycle by design, deploy, and manage internal tooling and automated systems that fundamentally accelerate the development, testing, and QA cycles of our backend and frontend engineering teams.

Secure, Offline-First AI System Design through architect and deploy intelligent systems that run reliably within restricted, local, or air-gapped server environments, ensuring complete data privacy and total alignment with national data sovereignty mandates.

Safely weave Large Language Models and automated decision systems into complex B2B workflow engines, compliance auditing systems, and data processing pipelines without introducing structural fragility.

Configure and optimize local routing proxies and intelligent middleware to manage model request execution, implement robust fallback matrices, and secure API boundaries across our internal management networks.

Implement and tune data storage architectures that support semantic search and high-speed retrieval, ensuring vector embeddings and indexing layers run efficiently within our core database environments.

Treat AI outputs as engineering metrics. You will design systems to log, measure, and analyze model latency, token efficiency, and reasoning accuracy, providing clear technical interpretations of model performance under production stress.

Requirements and Education

Proven track record of moving beyond simple API prototyping to embedding open-source or localized language models directly into production enterprise software architectures.

Highly proficient in backend systems programming, data structures, and standard software development workflows, enabling you to write clean, maintainable integration code.

Solid understanding of how containerized environments, secure subnets, and local server hardware interact when deploying resource-intensive AI models.

A strong engineering instinct that favors simple, auditable, and reliable automated workflows over unnecessarily complex or unpredictable AI implementations.

Added Advantages

• Experience setting up and optimizing localized model routing layers, load-balancing systems, or custom API fallback gateways.
• Direct experience running vector indexing extensions directly inside relational database management systems at scale.
• Background in automating continuous integration/continuous deployment (CI/CD) pipelines or creating automated testing frameworks using applied LLMs.

Education Requirements

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Computer Engineering, or a highly technical, systems-oriented discipline.

Equivalent and proven practical experience of deploying localized AI architectures into highly secure, production enterprise environments, your demonstrated engineering capabilities will be valued over formal degrees.

• Design, deploy, and manage internal tooling and automated systems that fundamentally accelerate the development, testing, and QA cycles of our backend and frontend engineering teams.
• Architect and deploy intelligent systems that run reliably within restricted, local, or air-gapped server environments, ensuring complete data privacy and total alignment with national data sovereignty mandates.
• Safely weave Large Language Models and automated decision systems into complex B2B workflow engines, compliance auditing systems, and data processing pipelines without introducing structural fragility.
• Configure and optimize local routing proxies and intelligent middleware to manage model request execution, implement robust fallback matrices, and secure API boundaries across our internal management networks.
• Implement and tune data storage architectures that support semantic search and high-speed retrieval, ensuring vector embeddings and indexing layers run efficiently within our core database environments.
• Design systems to log, measure, and analyze model latency, token efficiency, and reasoning accuracy, providing clear technical interpretations of model performance under production stress.

• Backend systems programming
• Data structures
• Standard software development workflows
• Containerized environments
• Secure subnets
• Local server hardware interaction
• Automated workflows
• Localized model routing layers
• Load-balancing systems
• Custom API fallback gateways
• Vector indexing extensions
• CI/CD pipelines
• Automated testing frameworks using applied LLMs

• Proven track record of moving beyond simple API prototyping to embedding open-source or localized language models directly into production enterprise software architectures.
• Highly proficient in backend systems programming, data structures, and standard software development workflows, enabling you to write clean, maintainable integration code.
• Solid understanding of how containerized environments, secure subnets, and local server hardware interact when deploying resource-intensive AI models.
• A strong engineering instinct that favors simple, auditable, and reliable automated workflows over unnecessarily complex or unpredictable AI implementations.
• Experience setting up and optimizing localized model routing layers, load-balancing systems, or custom API fallback gateways.
• Direct experience running vector indexing extensions directly inside relational database management systems at scale.
• Background in automating continuous integration/continuous deployment (CI/CD) pipelines or creating automated testing frameworks using applied LLMs.