About the Company

DescriptionLiquidity is the world's leading AI-powered private credit firm, pioneering a new standard in growth capital through a nexus of expertise in private credit and technology. With a global reach and regional depth across North America, Europe, APAC and MENA, Liquidity supports high-growth companies by deploying capital with speed, precision, and adaptability. Powered by proprietary decision science technology, Liquidity enables faster, more accurate investment decisions at scale.

About the Role

We are looking for a highly experienced, hands-on Principal AI Engineer with strong software engineering fundamentals and proven experience integrating and delivering AI-powered solutions in production environments. The ideal candidate understands how complex software systems work, can reason through architectural challenges, and knows how to adopt AI technologies without compromising reliability, performance, security, or maintainability. This is an engineering-first role, where critical thinking, problem-solving, and sound technical judgment are more important than familiarity with specific AI tools or frameworks. We value engineers who can take AI initiatives from experimentation to production, evaluate their effectiveness, and continuously improve their quality and reliability.

Responsibilities

• Drive the adoption and integration of AI capabilities across applications, systems, and engineering workflows.
• Design and implement AI-powered solutions using sound software engineering principles and architectural practices.
• Apply context engineering techniques to effectively manage information retrieval, application state, context quality, and AI interactions.
• Design and evolve AI application infrastructure, including orchestration, execution management, state management, observability, and reliability.
• Define and implement AI evaluation strategies, including quality metrics, benchmarks, automated evaluations, regression testing, and continuous improvement.
• Evaluate architectural and implementation trade-offs, balancing AI capabilities with deterministic software engineering.
• Ensure AI solutions meet production standards for scalability, security, performance, maintainability, and operational efficiency.
• Critically review AI-generated code, designs, and technical solutions, validating correctness rather than relying blindly on generated outputs.
• Identify engineering risks, challenge assumptions, and drive well-reasoned technical decisions.

Qualifications

• 10+ years of professional software engineering experience, with strong backend engineering fundamentals, distributed systems knowledge, and system architecture expertise.
• Minimum 2 years of hands-on AI engineering experience, with demonstrated experience designing, integrating, deploying, and operating AI-powered solutions in production—not limited to prototypes or proofs of concept.
• Strong understanding of APIs, asynchronous processing, concurrency, data management, system integration, and application lifecycles.
• Solid programming and debugging skills, with the ability to understand, review, and troubleshoot complex software systems.
• Practical understanding of modern AI technologies, their integration patterns, capabilities, limitations, and production challenges.
• Experience with context engineering, including information retrieval, context management, and optimization of AI interactions.
• Understanding of application infrastructure, including execution workflows, state management, failure handling, resilience, and observability.
• Hands-on experience evaluating AI systems, including defining quality metrics, establishing benchmarks, implementing automated evaluations, regression testing, and measuring accuracy, reliability, and performance.
• Experience with cloud-native architectures, modern software development practices, testing, and production operations.
• Excellent analytical thinking, problem-solving, and architectural decision-making skills.

Required Skills

• Strong backend engineering fundamentals and distributed systems knowledge.
• System architecture expertise.
• APIs, asynchronous processing, concurrency, and data management.
• System integration and application lifecycle understanding.
• Programming and debugging skills for complex software systems.
• Practical understanding of modern AI technologies and their production challenges.
• Context engineering, including information retrieval and context management.
• Application infrastructure knowledge, including execution workflows, state management, failure handling, resilience, and observability.
• Evaluation of AI systems with quality metrics, benchmarks, automated evaluations, and regression testing.
• Experience with cloud-native architectures and modern software development practices.
• Experience taking AI initiatives from experimentation to production.
• Experience continuously improving the quality and reliability of AI-powered solutions.

What We Value

We are looking for a strong software engineer who knows how to engineer AI systems, not simply an AI tools specialist.

The ideal candidate:

• Thinks independently: Challenges assumptions, reasons through complex problems, and makes informed engineering decisions.
• Understands systems deeply: Knows how software works internally, identifies potential failure points, and understands architectural trade-offs.
• Has real production experience: Has successfully moved AI solutions from experimentation to production and understands the challenges involved.
• Measures quality: Uses systematic evaluations, measurable outcomes, and continuous testing rather than subjective assessments.
• Applies AI pragmatically: Understands when AI adds value and when conventional engineering approaches are more appropriate.
• Takes ownership: Can independently design, implement, evaluate, and improve reliable AI-powered solutions.
• Remains hands-on: Is comfortable writing code, investigating failures, reviewing implementations, and solving difficult technical problems.