Note: The job is a remote job and is open to candidates in USA. The Phoenix Group is seeking a Lead Full Stack Engineer to build and scale an AI-first platform that turns organizational knowledge into actionable intelligence. The role leads end-to-end product development across backend services, data models, frontend architecture, and AI-powered user experiences, in partnership with Product, Design, and Data teams.
Responsibilities
- Own the design and delivery of end-to-end product experiences, from the backend services and data models that power them to the user-facing features built on top
- Collaborate with Product and Design to translate business requirements into intuitive, high-quality user experiences, evolving underlying services and interfaces to support changing needs
- Define and evolve canonical data models, shared domain abstractions, and REST/GraphQL APIs that are scalable, intuitive, secure, and capable of enabling rich, data-driven product experiences
- Model and manage complex entity relationships, including graph-like structures and hierarchical data, and expose them through interfaces that users can easily understand and navigate
- Build and maintain event-driven architectures, including publish/subscribe and streaming systems, to support loosely coupled, extensible services and responsive product behavior
- Implement fine-grained access control patterns, including role-based and attribute-based access control (RBAC/ABAC), ensuring secure and auditable data access across services and applications
- Design and integrate indexing and search capabilities for retrieval, filtering, and AI-enabled use cases, incorporating them into product experiences such as search, copilots, and recommendations
- Integrate AI and machine learning capabilities into product workflows with a strong focus on usability, reliability, and measurable business value
- Establish and evolve frontend architecture across applications, including framework selection, project structure, engineering conventions, state management, data fetching, performance, and accessibility
- Partner with Design to evolve the design system and develop and maintain shared UI component libraries and frontend core modules that accelerate product delivery
- Ensure reliability, scalability, and performance across backend services and user-facing features through observability, automated testing, and continuous improvement informed by feedback and usage data
- Drive developer experience improvements that increase engineering velocity and consistency across teams, enabling engineers to adopt and extend shared capabilities effectively
- Collaborate with DevOps and Security teams to implement secure, reliable, and compliant engineering practices throughout the stack
- Mentor engineers through architecture discussions, design reviews, code reviews, and ongoing technical guidance
Skills
- Strong systems thinker with a product mindset, capable of designing abstractions that scale across teams and use cases while remaining focused on user value and business outcomes
- Clear communicator who can explain complex data models, APIs, architectural decisions, and technical tradeoffs to diverse stakeholders
- Highly collaborative, with the ability to enable other teams through well-designed shared capabilities while also delivering end-to-end features across functional boundaries
- Execution-focused and pragmatic, with a strong sense of ownership over quality, reliability, and outcomes across the full stack
- Curious and adaptable, particularly in applying AI and large language model capabilities to structured data systems and real-world workflows
- 5+ years of professional software engineering experience, with strong backend and full-stack development capabilities
- Proficiency in Python and TypeScript, with hands-on production experience using backend frameworks such as FastAPI or Django and frontend frameworks such as React, Vue, or Angular, typically with a modern build tool such as Vite
- Proven experience delivering user-facing products and features end-to-end, including the shared services, APIs, and data models that support them
- Strong expertise in data modeling across relational and non-relational systems, with experience in graph-based or relationship-intensive domains
- Deep experience designing, building, and consuming REST/GraphQL APIs and implementing service-oriented architectures for production applications
- Solid understanding of frontend architecture, state management, and performance optimization, including the development of intuitive interfaces for complex, data-rich domains
- Experience with event-driven systems, such as Kafka or cloud-based publish/subscribe services, and asynchronous processing patterns
- Strong understanding of access control systems, including RBAC, ABAC, and multi-tenant application design
- Experience designing or integrating search and indexing systems, including Elasticsearch and vector search technologies
- Solid experience with cloud-native systems, particularly AWS, alongside CI/CD pipelines and observability practices
- Familiarity with AI/ML-enabled systems and their integration into product experiences, including retrieval-augmented generation (RAG), entity resolution, semantic search, copilots, and recommendation systems
- Actively leverages AI coding assistants and agents to augment everyday engineering work, treating fluency with these tools as an essential part of modern software development
- Demonstrates sound judgment in evaluating AI-generated code, understanding when to trust agent output, when to conduct deeper reviews, and when direct engineering intervention is necessary
- Comfortable orchestrating multiple AI agents across independent workstreams and efficiently integrating their outputs into cohesive solutions
- Continuously identifies opportunities to improve AI agent harnesses through better tooling, context management, guardrails, and feedback loops that increase agent autonomy and reliability over time
- Bachelor's degree in Computer Science, Software Engineering, or a related technical discipline, or equivalent practical experience
- Experience with AI workflow observability and evaluation is an advantage
- Experience working with data-intensive industries or domains, such as property, financial services, investment management, or enterprise analytics, is an advantage
- Advanced degree preferred
- Cloud certifications in architecture, engineering, or security across AWS, Azure, or Google Cloud are considered an asset
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