Job description

About Janus DigitalJanus Digital is the agentic AI platform that runs the world's real assets.
Based in the UAE with global operations, our enterprise platform acts as the central operating system for intelligent buildings, and we earn only on the additional value we create.
Buildings account for nearly 40% of global energy consumption and carbon emissions, yet most are still run by fragmented systems that react to problems instead of preventing them.
We exist to change that, proving that AI for Good is also good business.
Unlike language models that can guess, our platform is grounded in physics: thermodynamics, pressure dynamics, energy conservation, and fluid mechanics.
It builds a live, millimetre-accurate picture of every structure, monitors how energy, water, and air move through every system in real time, anticipates maintenance weeks ahead, and makes thousands of autonomous micro-adjustments around the clock.
It calculates; it does not guess.
The technology is not new, it is battle-hardened: development began in 2009 and has matured through four generations of real-world deployment, built on 15+ years of foundational R&D, 76 granted patents, and over $100M invested.
The platform is proven across 10,000+ building datasets in 253 cities, trusted by Fortune Global 500 companies, sovereign wealth funds, and premium hospitality brands, spanning hospitals, airports, malls, theme parks, power grids, and manufacturing.
Typical outcomes include around 29% energy savings, 99% equipment-risk detection before failure, and full deployment in 14 days with no phased rollout.
About this role This role sits within the AI Office, the team responsible for designing, building, and running Janus Digital’s AI platform and the engineering capability behind it.
Reporting to the Head of AI Office, the engineer joins a small, senior group working at the core of how the company applies artificial intelligence across its global operations.
We are looking for a full-stack AI engineer who combines hands-on software delivery with genuine platform depth—someone who has built and operated systems to a banking-grade standard and is fluent across modern cloud infrastructure, AWS in particular.
That background is central to the work: the AI Office ships production systems the wider organisation depends on, where reliability, security, and clean architecture are not optional.
The same instincts that keep banking and financial platforms dependable are exactly what this role demands.
As part of the AI Office, the engineer will own meaningful parts of the platform end to end—backend services, user-facing interfaces, identity and access, and the AI agent and integration layers—and will work alongside existing engineers and the CTO function on cloud deployment and reliability.
The objective is a platform engineered to a product standard: secure, scalable, observable, and built to evolve as the company and the field do.
This engineer is central to making that real.
What you'll do Build and own the product across the stack: backend services and APIs in Python and TypeScript; frontend interfaces in React (or equivalent) for web dashboards, administrative surfaces, and AI agent interaction layers.
Build cloud-native software on AWS (primary), and across the wider cloud ecosystem where relevant, that assumes it will run in a managed, containerized cluster environment.
Design and implement enterprise-grade authentication and authorization: SSO (SAML/OIDC), SCIM provisioning, RBAC, and fine-grained IAM.
Design the platform for multi-tenancy from the ground up: configuration isolation, provisioning flows, usage metering, and the operational tooling needed to run it reliably at scale.
Build and deploy production AI agents using MCP and streaming APIs; design multi-model orchestration across Claude, Gemini, and other models as the landscape evolves.
Contribute to and evolve the memory and knowledge architecture at the core of the platform—a structured, multi-graph, file-based system underpinning institutional intelligence across the company.
This is among the hardest and most consequential engineering problems on the platform.
Architect the platform as a composition of isolated, independently deployable microservices—each functional domain (finance tools, marketing stack, knowledge layer, agent runtime) a discrete service with well-defined APIs between them.
Competitive compensation with performance-based incentives tied to delivery quality and client outcomes.
A specialized domain-expert role at the absolute frontier of Agentic AI and autonomous building intelligence.
Hands-on engagement with prestigious, diverse real estate portfolios (commercial offices, hotels, malls, and mixed-use developments).
Direct collaboration with our global Head of AI team.
Clear career progression paths into senior consulting or regional delivery leadership roles.
Sponsorship and support for professional development, including certifications in building services and energy management.
What we're looking for 5+ years in a full-stack software engineering role, with shipped, production systems at scale—backend and frontend both visible in your GitHub history.
Direct experience building or operating banking, fintech, or comparable high-reliability, regulated software; you understand first-hand the reliability, security, and compliance bar these environments demand.
Broad, hands-on cloud experience: AWS (primary), with working knowledge across the wider cloud ecosystem (GCP/Azure).
You architect cloud-native systems, not just deploy to a single VM.
Containerization and cloud-native deployment in production: Docker and Kubernetes; you write software that assumes it will run in a managed cluster environment.
Strong Python and TypeScript; React or an equivalent modern frontend framework; genuine comfort moving between layers of the stack.
Demonstrable multi-tenant and/or distributed-systems experience: tenant isolation, provisioning flows, and the operational tooling that keeps distributed systems running in production.
Enterprise identity and access management implemented in production—SSO (SAML/OIDC), SCIM, RBAC, Okta or Azure AD—not just read about.
API design for external consumers: versioning, authentication, rate limiting, and documentation standards.
Security fundamentals: zero-trust principles, data isolation, audit logging, DLP—security as a design constraint, not a post-launch checkbox.
AI/LLM integration: MCP server development, function calling, streaming APIs, and multi-model orchestration.
Fluent with agentic development tooling—Claude Code, Codex, and CLI-based AI workflows—used with discipline and judgment, not as a substitute for engineering thinking.
Active GitHub profile mandatory—applications without a demonstrable commit history will not be considered.
Nice to have Experience with core banking, payments, or other regulated financial systems (transaction integrity, settlement, KYC/AML data flows, and similar).
Vertex AI / Google AI Studio / Claude API / Gemini Enterprise.
Slack Apps (Bolt Framework, Block Kit) and Google Workspace integrations.
Agent memory patterns, structured knowledge graphs, or evolving AI context-management architectures.
Regional compliance experience (GDPR, Singapore PDPA, UAE data protection frameworks).
Familiarity with Terraform or equivalent IaC tooling (ownership sits with the infrastructure team, but fluency helps).
Go or Rust for performance-critical components.
Experience taking an internal platform or product through significant scale or its first external users.