Software Engineer

Aquent is proud to partner with a pioneering technology innovator to find a talented engineer who will revolutionize how development teams build and scale software. In this role, you will have a profound impact on developer productivity across the entire organization, architecting robust, scalable solutions that empower teams to harness the power of AI without extensive re-architecture. This is your opportunity to shape the future of internal development ecosystems, setting new standards for efficiency, intelligent automation, and cutting-edge developer tools.

About the Partner

Our client is at the forefront of pioneering technological advancements, fostering a culture of creativity, collaboration, and technical excellence. Dedicated to empowering engineers to deliver cutting-edge solutions more efficiently, they are building a state-of-the-art shared platform that integrates advanced AI agent technology. By joining this forward-thinking team, you will be part of an environment that thrives on navigating ambiguity, continuous innovation, and solving complex, real-world engineering challenges.

The Opportunity

In this dynamic position, you will dive into the heart of the internal development ecosystem, designing, building, and extending a shared platform that integrates state-of-the-art AI agent technology. You will champion extensibility as a core feature, ensuring seamless integration of new triggers, agents, and tools. By maintaining a deep empathy for the developer experience, you will accelerate innovation and transform how engineering teams operate daily. This role offers continuous opportunities for problem-solving in a non-deterministic, empirical environment where adaptive debugging and creative thinking are highly valued.

Key Responsibilities

• Architect and implement sophisticated agent orchestration systems, including router functions, queueing mechanisms, and agent invocation services.
• Seamlessly integrate AI agent capabilities into modern continuous integration and continuous delivery (CI/CD) platforms, designing pipeline configuration patterns where agent invocations execute as stages, process context from code changes, produce artifacts, and control subsequent stages based on AI agent outcomes.
• Develop and expand a suite of shared, tool-based functions that empower AI agents with versatile capabilities.
• Design and implement advanced knowledge bases, leveraging powerful search and storage technologies to ensure AI agents can reason over a comprehensive, shared organizational context rather than isolated prompts.
• Implement robust AI guardrails, including input/output filters, sensitive data scrubbing, content moderation, and fine-grained, per-agent permission boundaries, ensuring secure and compliant AI operations by design.
• Develop and integrate resource management controls for efficient resource utilization, encompassing model tiering and intelligent routing via a large language model gateway, semantic caching, and optimized context-window management.
• Instrument the entire AI platform with comprehensive observability, including dashboards, audit trails, and detailed trace logging for every agent invocation (input, output, decision, tokens, cost) to ensure compliance, facilitate debugging, and provide deep operational insights.

Must-Have Qualifications

• 3 to 7 years of professional experience in software development.
• Strong proficiency in Python and its SDK for building production-grade serverless functions and event-driven services.
• Solid experience with cloud-based services, including serverless compute, API management, messaging queues, event buses, identity and access management (IAM), secrets management, and monitoring tools.
• Specific experience with AI agent services, action groups, knowledge bases, and guardrails.
• Hands-on experience integrating with large language model (LLM) APIs.
• Familiarity with continuous integration/continuous delivery (CI/CD) platforms and best practices.
• A strong security mindset, including expertise in least-privilege access, secure secrets handling, input validation, and an awareness of prompt injection and data-leak risks in LLM workflows.
• Comfortable with observability principles, including structured logging, metrics, tracing, and writing highly debuggable code for production environments.

Nice-to-Have Qualifications

• Experience with vector search and Retrieval-Augmented Generation (RAG), including vector databases, embeddings, and retrieval evaluation.
• Familiarity with financial operations or resource management concepts, such as cost attribution, intelligent model routing, semantic caching, and batch inference.
• Experience building developer platforms or internal tooling that other engineers rely on daily.
• Familiarity with advanced AI agent frameworks and an understanding of their architectural tradeoffs.