Senior AI Native Engineer (Python, SQL, LLM, AWS)

Top 3 reasons to join us

• AI-native culture with enterprise AI tools
• Flexible hybrid work, focused on outcomes
• Small team, high ownership

Job description

What This Role Is

We are building AI-native delivery systems without a fixed precedent. AI-augmented engineering and agentic workflows are shifting the core constraint from building software to designing, orchestrating, and improving intelligent systems that can reason and act.

As a Senior AI Native Engineer, you will own end-to-end AI product surfaces—from ideation to production—working across product, engineering, and applied AI systems to turn ambiguity into working, scalable solutions.

You will work closely with internal stakeholders on rapid experimentation, prototyping, and Lab based initiatives that demonstrate and validate new agentic capabilities.

From a technical standpoint, you should be able to engage meaningfully with product teams and understand core system design concepts. This includes having a realistic understanding of AI capabilities and limitations, recognizing why certain problems are more complex than they appear, and understanding how system performance and success are evaluated in production environments. While implementation sits with engineering, you are responsible for the correctness of what gets built. That means reviewing technicalproposals, interrogating assumptions, and ensuring that every solution is feasible, well-structured, and grounded in real system behavior with the engineering and delivery team. Concretely, you are responsible for four things:

End-to-End AI System Ownership

• Lead the end-to-end design and development of AI-native products, including agents, workflows, and intelligent systems built on modern cloud architectures
• Own and implement AI-driven pipelines and workflows, ensuring reliability, scalability, reusability, and production readiness
• Design system architectures for AI applications, including agent frameworks, RAG pipelines, and multi-step reasoning workflows
• Collaborate closely with engineers, data scientists, and stakeholders to deliver AI-powered features, decision systems, and automation solutions.
• Oversee integration of structured and unstructured data sources, ensuring strong governance, reliability, and system integrity
• Establish best practices around code quality, testing, documentation, and CI/CD for AI systems and workflows
• Contribute to development of AI agents and intelligent services leveraging real-time data, APIs, and vector-based retrieval systems
• Optimize system performance, latency, cost, and scalability across AI pipelines and infrastructure layers
• Work directly with global stakeholders, gathering requirements and translating them into scalable AI product solutions
• Mentor junior builders and contribute to a strong engineering and AI product culture

Your skills and experience

What We Are Looking For

• 5+ years of experience in software engineering, AI product development, or data-driven system engineering
• Demonstrated experience designing and delivering production AI systems, including agents, automation pipelines, or intelligent workflows
• Strong foundation in AI system architecture, including LLM integration, retrieval-augmented generation (RAG), and workflow orchestration
• Advanced proficiency in Python and SQL for developing reliable, scalable AI-enabled systems
• Hands-on experience with major cloud platforms (AWS, Azure, or GCP) in production environments
• Experience integrating APIs and building service-oriented or event-driven architectures
• Strong knowledge of data engineering concepts, including handling structured and unstructured data at scale
• Excellent communication skills in English, capable of working with different teams and senior stakeholders
• Proven leadership ability with strong problem-solving skills and full lifecycle ownership of technical initiatives

You Will Work Across

This role spans multiple domains depending on product needs:

• AI agents and multi-step autonomous workflows
• RAG systems over messy, real-world data (e.g., planning documents, regulations, reports)
• Decision intelligence systems and spatial AI applications for real estate and operational use cases
• Full-stack product development with engineering and delivery team (frontend interaction design, backend systems, and API integration)
• Rapid prototyping of new ideas, tools, and internal systems
• Any component requir…