Principal AI Engineer

At HSP Group, we're pioneering a new niche within the fast-growing industry of global expansion management. As a Stage B startup, we focus on delivering value through relentless innovation, execution, and customer delight. As part of a small but highly experienced and dynamic team, you will play a critical role in our company's success.

Gateway HR is our HRIS platform designed specifically for global expansion management. It offers deep integration with Gateway GXM — our flagship global entity management platform — enabling capabilities like automatic payslip generation, intelligent contract management, and seamless cross-border compliance.

Our platform ecosystem already includes AI-powered document recognition and classification in production, event-driven serverless architecture on Azure, and a growing suite of integrated tools spanning HR, entity management, and compliance.

Join our dynamic and growing team at HSP Group as a Principal AI Engineer. In this role, you'll lead the adoption of AI across the engineering function — designing agentic workflows, building RAG systems, and guiding engineers to become architects of intent rather than just writers of code. You'll work directly with technology and engineering leadership to prototype and deploy AI-powered solutions, translating business problems into scalable architectures from proof-of-concept through to production.

You will partner closely with Engineering management, Product, CX, and other business stakeholders to deliver secure, scalable, customer-centric AI solutions. Additionally, you will pioneer AI within the product delivery team, experiment and implement agentic coding and testing tools, lead the development of AI features in our products, and provide AI thought leadership across the organization. Success in this role will come from a mix of technical depth, strong execution, sound judgment, and the ability to collaborate with internal and external teams. This role reports to the EVP, Chief Technology Officer or designated engineering manager.

• →Evaluate, implement, and guide the effective use of AI tools within the software development lifecycle.
• →Establish best practices for AI development, coding assistance, coding agents, AI testing, documentation, debugging, and engineering workflow optimization.
• →Train engineers to become "architects of intent" rather than just writers of code—focusing on providing clear, high-level, context-rich goals, constraints, and validation criteria for AI agents to execute.
• →Institutionalize a culture of validation, requiring human engineers to thoroughly review, test, and understand AI-generated code to reduce risk of production issues.
• →Deploy multi-agent workflows using dedicated agent teams (e.g., separate agents for planning, coding, and testing) operating in parallel to automate end-to-end tasks like PR generation, refactoring, or legacy modernization.
• →Integrate agents directly into error monitoring systems and bug reporting (Jira) to automatically ingest stack traces, locate the root cause across the codebase, and generate a verified PR with a corresponding regression test prior to engineering triaging the ticket.
• →Prioritize refactoring for high modularity, deterministic testing, and explicit documentation to ensure agents can navigate, understand, and safely modify code, treating codebase health as the foundation for AI capability.
• →Mentor members of the Engineering team, supporting their growth, accountability, and day-to-day effectiveness using AI tools.
• →Partner closely with Product Management, CX leaders, and other stakeholders to translate business needs into high-quality technical solutions.
• →Ensure AI development meets a high bar for software quality, security, scalability, and reliability.
• →Design scalable AI/ML pipelines using LLMs, RAG, and agentic frameworks and integrate AI APIs into customer-facing applications and workflows.
• →Develop reusable accelerators, templates, and reference architectures to be leveraged by the broader engineering team.

• Proven track record of moving beyond basic LLM chat interfaces to implementing complex agentic frameworks that automate multi-step engineering workflows.
• Experience architecting RAG (Retrieval-Augmented Generation) systems and sophisticated prompting strategies to provide agents with the high-fidelity codebase context necessary for accurate planning and execution.
• Experience evaluating or applying AI-enabled engineering tools and workflows, and a desire to experiment and capitalize on emerging AI technologies.
• Experience leading a cultural transition where engineers view agents as force-multipliers rather than replacements, encouraging a shift from manual "gatekeeping" to a "co-pilot" mindset.
• Strong hands-on experience building and supporting B2B SaaS products in a cloud-based environment.
• Experience leading engineering delivery for production applications, including architecture, design, development, and operations.
• A practical, delivery-oriented mindset with strong technical judgment and a willingness to stay close to the work.
• Experience working with and overseeing third-party engineering partners or vendors.
• Strong knowledge of Azure, along with modern development and deployment practices.
• Solid understanding of Agile and DevOps methodologies, with the ability to apply both strategically and practically.
• Strong communication and stakeholder management skills, with the ability to work cross-functionally and keep teams aligned.
• Ability to explain complex AI concepts clearly to non-technical stakeholders.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.