Orbox

AI-First Senior Software Engineer

Ukraine, Kyiv  On-Site · Full-Time

Company: Orbox.ai - AI-Native PropTech Operating System for Real Estate

Location: Ukraine, Kyiv - on-site, office in the city centre

Employment: Full-time, permanent

Seniority: Senior

Reports to: Tech Lead

Works with: AI-Engineer, Product Analyst, UI/UX Designer, QA Engineer

Mobilization reservation: Full reservation (deferment) from mobilization for male employees

PropTech experience: Not essential, very good if has direct or indirect experience

English: Advanced (B2-C1)

About

We are building an AI-native operating system for real estate. The product is a PropTech platform where LLMs and AI agents are not bolted on top - they are the architecture. Owners, operators and guests interact with the AI system through natural language. AI autonomously generates compliance documents, routes staff, answers guest queries from a RAG-indexed knowledge base in real time and orchestrates financial split-payments.

But the product is only half of it.

We are equally an AI-first delivery organization. The way we discover, define and ship is as differentiated as what we ship. Specs, stories and designs are machine-readable artifacts our coding and review agents consume directly - and senior engineers are the ones who design the system, direct the agents and own every line that reaches production.

The role

This is the role that turns machine-readable specs into production software - by leading AI agents, not by typing every line yourself.

You will report directly to the Head of Delivery & Product, with short decision loops to founders and direct access to product, design and QA.

This is a hands-on senior engineering role at the core of the delivery machine. Most of our code is written by AI coding agents against machine-readable specs. That makes senior engineering judgment more valuable, not less: agents write fast, but still pick wrong abstractions, miss edge cases, leak data across tenants and ship code that passes its own tests and breaks in production. You are the engineer who designs the system, sets the guardrails and owns what gets merged.

You will build and ship features end to end across backend, frontend and integrations: service design, data models, APIs, UI, government and third-party integrations, AI features (LLM agents, RAG, document generation) and the infrastructure they run on.

You will work side by side with the AI-First Product Analyst (who writes the specs), Product Designers (who own the mockups) and QA (who holds the release bar). You will use our shared Claude Team / Claude Code with premium seating along with the company's AI tools daily, deeply and natively - not as a novelty, but as the default way you work.

We operate in a fast-moving startup environment where speed and precision both matter. You must be opinionated about which engineering tasks AI does well today (boilerplate, refactors, tests, migrations, docs), which it does not (architecture, security boundaries, trade-off calls, debugging subtle production issues) - and how to combine both deliberately.

What you will own & do

AI-First Engineering

• Daily, deep, native use of AI coding agents for implementation from specs, refactoring, test writing, code review, debugging and documentation.

• Treat agents like a team you lead: clear specs in, reviewed code out - you own the context, rules files, guardrails and the review bar.

• Build and improve our agentic delivery pipeline: repo conventions, agent instructions, MCP tooling and CI checks that keep agent output safe to merge.

• Contribute to internal evals from the engineering side: define what “good” looks like for AI-generated code and for our user-facing AI features.

Product Engineering

• Own features end to end: from spec & acceptance criteria to design, implementation, tests, deployment and monitoring on prod.

• Design clean service boundaries, data models and APIs for a multi-tenant B2B2C platform with role-based access.

• Build complex frontend surfaces: dashboards, real-time data, multi-step workflows, back-office tooling.

• Integrate with government and regulatory APIs, payment providers and third-party systems - reliably, idempotently, with proper error handling and audit trails.

AI & LLM Systems

• Build production AI features: LLM agents, tool calling, RAG pipelines, compliance document generation - with grounding, fallbacks and cost control.

• Engineer the non-deterministic layer: prompt versioning, structured outputs, guardrails against hallucinations and prompt injection, tenant-safe retrieval.

• Instrument AI behaviour with tracing, logging and metrics - treat AI output as something to be measured, not trusted.

Engineering Quality & Ownership

• Own the merge bar: review agent and human PRs, enforce architecture, security and performance standards.

• Write automated tests and CI that make agent output safe to ship; partner with QA on test strategy and release readiness.

• Feed spec & story defects back upstream: when the work item is wrong or ambiguous, it gets fixed at the spec layer, not hacked around in code.

• Shape the engineering practice at an early stage: conventions, environments, observability, incident handling - lightweight enough for startup speed, rigorous enough to trust.

What we are looking for

Must have

• 4+ years of hands-on software engineering on production web products, with senior-level ownership of a system or major product area.

• Strong full-stack skills: modern backend (TypeScript / Node.js or Python), modern frontend (React / Next.js or similar) and relational databases (PostgreSQL or similar).

• Daily, deep, native use of AI coding tools (Claude Code, Cursor or similar) in your engineering workflow. You have a real, opinionated workflow built around them and can defend your choices.

• Hands-on experience building LLM-based features: agents, tool calling, RAG, structured outputs - shipped to production, not just demos.

• Solid system design: multi-tenant SaaS, role-based access, API design, async processing, third-party integrations.

• Critical code review skills: you read AI-generated code with suspicion and catch what agents miss - security holes, data leaks, wrong abstractions, hidden complexity.

• Cloud & DevOps fluency: Docker, CI/CD, at least one major cloud (AWS / GCP / Azure), logging and monitoring.

• 0 → 1 mindset: comfortable in an early-stage team, working directly with founders and product, shipping fast without losing rigor.

• Strong written and verbal communication in English - including specs, PR descriptions and technical discussions.

Strong advantage (Nice to have)

• Background in PropTech, hospitality tech, Fintech or multi-sided marketplace platforms.

• Exposure to payment flows, compliance / regulatory workflows, document-generation systems or government API integrations.

 Experience with LLM evals and observability (Langfuse, LangSmith or similar) and vector databases.

• Real-time systems, IoT or tokenization / blockchain infrastructure.

Why this role

You will help build the first AI-native operating system for real estate in Dubai and define how software gets built when AI agents write most of the code.

• Genuine engineering ownership.

• Full reservation from mobilization for male employees.

• Clear growth trajectory - Tech Lead / Engineering Lead.

• Direct access to founders and Head of Delivery & Product.

• Technically ambitious product surface - AI-native, Agentic system, RAG, IoT, real-time systems, government APIs, tokenization.

• AI-first by default.

• Great office in the centre of Kyiv.

AI-First Senior Software Engineer

Ukraine, Kyiv · On-Site · Full-Time

Company: Orbox Group - new AI-native product for the oil & petroleum business in Ukraine

Location: Ukraine, Kyiv - on-site, office in the city centre

Employment: Full-time, permanent

Seniority: Senior

Reports to: Tech Lead

Works with: Business Analyst, UI/UX Designer, QA Engineer, business owners & operations team

Mobilization reservation: Full reservation (deferment) from mobilization for male employees

Languages: English - Advanced (B2-C1); Ukrainian - fluent

The role

This is the role that turns a real offline business into production software - by leading AI agents, not by typing every line yourself.

This is a hands-on senior engineering role. Most of our code is written by AI coding agents against machine-readable specs. That makes senior engineering judgment more valuable, not less: agents write fast, but still pick wrong abstractions, miss edge cases, get money and inventory maths wrong and ship code that passes its own tests and breaks in production. You are the engineer who designs the system, sets the guardrails and owns what gets merged.

We operate in a fast-moving startup environment where speed and precision both matter. You must be opinionated about which engineering tasks AI does well today (boilerplate, refactors, tests, migrations, docs), which it does not (architecture, security boundaries, trade-off calls, debugging subtle production issues) - and how to combine both deliberately.

What you will own & do

AI-First Engineering

• Daily, deep, native use of AI coding agents for implementation from specs, refactoring, test writing, code review, debugging and documentation.

• Treat agents like a team you lead: clear specs in, reviewed code out - you own the context, rules files, guardrails and the review bar.

 Set up the agentic delivery pipeline for the new product from day one: repo conventions, agent instructions, MCP tooling and CI checks that keep agent output safe to merge.

• Contribute to internal evals from the engineering side: define what good” looks like for AI-generated code and for the product's user-facing AI features.

Product Engineering

• Own the architecture from zero: service boundaries, domain and data model, APIs, environments - built for a real operational business with multiple sites and roles.

• Own features end to end: from spec & acceptance criteria to design, implementation, tests, deployment and monitoring on prod.

• Build operational surfaces: dashboards, inventory and stock movements, orders, pricing, documents, approvals, reporting and back-office tooling - including mobile-friendly flows for people in the field.

Integrations & Data

• Integrate with the existing world: accounting and ERP systems, Excel and legacy data, banks and payment providers, fiscalization and regulatory reporting.

• Connect the physical side of the business where relevant.

• Plan and run data migration from spreadsheets and legacy tools - reliably, idempotently, with proper error handling and audit trails.

AI & LLM Systems

• Build production AI features: LLM agents, tool calling, RAG, document processing (waybills, invoices, contracts), reconciliation, forecasting and anomaly detection - with grounding, fallbacks and cost control.

• Engineer the non-deterministic layer: prompt versioning, structured outputs, guardrails against hallucinations and prompt injection, role-safe access to data.

• Instrument AI behaviour with tracing, logging and metrics - treat AI output as something to be measured, not trusted.

Engineering Quality & Ownership

 Own the merge bar: review agent and human PRs, enforce architecture, security and performance standards.

 Write automated tests and CI that make agent output safe to ship; partner with QA on test strategy and release readiness.

• Feed spec & story defects back upstream: when the work item is wrong or ambiguous, it gets fixed at the spec layer with the Business Analyst, not hacked around in code.

• Shape the engineering practice for the product: conventions, environments, observability, incident handling - lightweight enough for startup speed, rigorous enough to trust.

What we are looking for

Must have

 4+ years of hands-on software engineering on production web products, with senior-level ownership of a system or major product area.

 Strong full-stack skills: modern backend (TypeScript / Node.js or Python), modern frontend (React / Next.js or similar) and relational databases (PostgreSQL or similar).

• Daily, deep, native use of AI coding tools (Claude Code, Cursor etc.) in your engineering workflow. You have a real, opinionated workflow built around them and can defend your choices.

• Hands-on experience building LLM-based features: agents, tool calling, RAG, structured outputs - shipped to production, not just demos.

• Solid system design for operational B2B software: transactional data, inventory and money flows, role-based access, API design, async processing, third-party integrations.

• Critical code review skills: you read AI-generated code with suspicion and catch what agents miss - security holes, data leaks, wrong abstractions, hidden complexity.

• Cloud & DevOps fluency: Docker, CI/CD, at least one major cloud (AWS / GCP / Azure), logging and monitoring.

• 0 → 1 mindset: comfortable starting a product from an empty repo, working directly with founders, business owners and product, shipping fast without losing rigor.

• Strong written and verbal communication in English and Ukrainian - including specs, PR descriptions, technical discussions and talking to the people who run the business.

Strong advantage (Nice to have)

• Experience integrating with BAS / 1C, Ukrainian banks and payment providers, fiscalization or tax / excise reporting.

• Document processing (OCR, extraction), forecasting or anomaly detection in production.

• Experience with LLM evals and observability (Langfuse, LangSmith or similar) and vector databases.

Why this role

You will own a new AI-native/AI-first engineering from an empty repo and prove that an AI-first delivery machine can transform a traditional industry, not just a startup niche.

• Genuine engineering ownership - you design the system from zero and hold the merge bar.

• Full reservation from mobilization for male employees.

 A real business as your first customer - live operations, real users, real volumes and money.

• Clear growth trajectory - Tech Lead / Engineering Lead for this product line.

• Direct access to founders and Head of Delivery & Product.

• AI-first by default.

• Great office in the centre of Kyiv.

AI-First Senior Business Analyst

Ukraine, Kyiv · On-Site  Full-Time

Company: Orbox

Product: AI-native software digitalizing an existing offline business

Location: Ukraine, Kyiv - on-site, office in the city centre

Employment: Full-time, permanent

Seniority: Senior

Reports to: Head of Delivery & Product

Works with: Business owners & operations team, Engineering team, Design team, AI agents

Mobilization reservation: Full reservation (deferment) from mobilization for male employees

Languages: Ukrainian - native / fluent; English - Upper-Intermediate+ (B2+)

The role

This is the role that defines what this new product is, why each part of it is worth building and how it is described to the machines that build it.

You will report directly to the Head of Delivery & Product and sit in one team with Orbox - same AI-first delivery machine, same tools, same standards. You will own product from the analysis side: discovery, process analysis, PRDs and agent-ready specifications.

This is a hands-on, 0 → 1 analytical role. You will dig into where the work happens. You will map as-is workflows, find where time, money leak and design the to-be processes where AI agents and software do the routine work and people make the decisions.

We operate in a fast-moving startup environment where speed and precision both matter. You must be opinionated about which analysis and documentation tasks AI does well today, which it does not and where in a traditional business AI creates real value versus where it is just noise.

What you will own & do

AI-First Way of Working

• Daily, deep, native use of AI tools for interview and field-note synthesis, process mapping, document analysis, PRD and spec drafting, competitor teardowns and data analysis.

• Treat AI tools the way an engineer treats their IDE: opinionated, tuned, integrated into every step of your workflow.

• Co-design the boundary between what you produce by hand and what is generated, transformed or validated by agents.

• Contribute to internal evals from the analysis side: define what “good” looks like for AI-generated artifacts and for the product's user-facing AI features.

Business Discovery & Process Analysis

• Own end-to-end discovery of the existing business: stakeholder and staff interviews, shadowing, field visits, document and data audits across departments.

• Map as-is workflows (BPMN or similar) and design to-be processes - roles, handoffs, documents, approvals, exceptions and the data behind each step.

• Find and size the opportunities: manual work, errors, delays, fuel and cash losses, reporting pain - and where AI agents can take over routine work (document processing, reconciliation, dispatch, forecasting, anomaly detection).

• Understand the regulatory and accounting side of the business (excise, fiscalization, tax and fuel reporting, primary documents) well enough to turn it into requirements.

• Evidence over opinion: maintain a living research repository so every product decision traces back to a real workflow, a real number or a real user.

PRDs & Specs for the AI-First Delivery Machine

• Own the PRD & spec layer for the product: vision, scope, modules, user stories, acceptance criteria, edge cases, data contracts and state behaviour.

• Convert validated requirements into clear, structured, machine-readable specifications our orchestrator and coding agents can implement, test and evolve reliably.

• Define the domain model: products, tanks and depots, vehicles and routes, counterparties, contracts, prices, documents - the single source of truth for engineering, QA and evals.

• Keep specs in sync with reality: when the business or product changes, the spec changes first.

Delivery, Rollout & Adoption

• Shape the roadmap and scope with the Head of Delivery & Product and business owners - what we digitalize first and why.

• Plan integrations and migration from existing tools and legacy systems (Excel, accounting software, equipment and sensor data where relevant).

 Run UAT with real users in the business, define success metrics per capability and track what actually changed after release.

• Drive adoption: training materials, rollout plans and feedback loops with staff who have worked offline for years.

What we are looking for

Must have

• 4+ years of relevant experience as a business analyst, systems analyst or product analyst, with senior-level ownership of discovery and requirements.

• Digital transformation experience: you know how to take real offline or legacy business processes (operations, logistics, finance, supply chain) and turn them into working software together with an engineering team.

• 0 → 1 builder: comfortable being the first and only analyst on a new product (for the start), working directly with business owners, Heads and engineering.

• Strong process analysis craft: interviews, shadowing, BPMN / process modeling, as-is / to-be design, requirements that hold up in the real world.

• Excellent product documentation: PRDs, user stories, acceptance criteria, edge cases, data models - clear, testable, unambiguous, structured enough that engineers (and agents) do not have to guess.

• Daily, deep, native use of AI tools in your analysis workflow. You have a real, opinionated workflow built around them and can defend your choices.

• Comfortable with complex operational B2B domains: inventory, logistics, back-office, finance and accounting flows, role-based access, reporting.

• Data literacy: comfortable reading data (Excel, SQL or BI tools) to back decisions and quantify the problem.

• Strong communication in Ukrainian and English (B2+): you can interview a depot operator in the morning and write a spec for an international engineering team in the afternoon.

Strong advantage (Nice to have)

• Knowledge of Ukrainian regulatory and accounting specifics: excise, fiscalization, tax reporting, primary documents; experience with BAS / 1C or ERP implementations.

• Experience defining specs or workflows for AI agents, RAG systems or document processing (OCR, extraction).

Why this role

• Genuine product ownership - you define the product from discovery to specs.

• Full reservation from mobilization for male employees - officially arranged by the company.

• A real business as your first customer - live operations, real users, real numbers.

• Clear growth trajectory: evolves into AI-First Product Manager and then Head of Product for this product line.

• Direct access to founders and Head of Delivery & Product.

• AI-first by default.

• Great office in the centre of Kyiv.

AI-First Senior QA Engineer (Manual)

Ukraine, Kyiv · On-Site · Full-Time

Company: Orbox.ai - AI-Native PropTech Operating System for Real Estate

Location: Ukraine, Kyiv  on-site, office in the city centre

Employment: Full-time, permanent

Seniority: Senior

Reports to: Technical Lead 

Works with: Product Analyst, Product Designer, Engineering team, Fleet of Agents

Mobilization reservation: Full reservation (deferment) from mobilization for male employees

PropTech experience: Not essential; very good if you have direct or indirect experience

English: Advanced (B2+)

About

We are building an AI-native operating system for real estate. The product is a PropTech platform where LLMs and AI agents are not bolted on top - they are the architecture. Owners, operators and guests interact with the AI system through natural language. AI autonomously generates compliance documents, routes staff, answers guest queries from a RAG-indexed knowledge base in real time and orchestrates financial split-payments.

But the product is only half of it.

We are equally an AI-first delivery organization. The way we discover, define and ship is as differentiated as what we ship. Specs, stories and designs are machine-readable artifacts our coding and review agents consume directly — and quality is verified by a human who knows exactly where agents fail, where specs lie and where the product breaks in real hands.

The role

This is the role that decides whether what we ship actually works  for humans and for the agents that built it.

You will report directly to the Head of Delivery & Product, with short decision loops to founders and direct access to engineering.

This is a hands-on, manual-first QA role at the core of the delivery machine. Most of our code is written by AI agents against machine-readable specs. That makes human quality judgment more valuable, not less: agents pass their own checks and still ship broken flows, wrong edge-case behaviour and UX that technically matches the spec but fails the user. You are the last honest reader of the product before it reaches staging review and then customers on prod.

You will own manual testing end to end: exploratory testing, spec-based test design, UAT coordination, regression passes and release sign-off. You will test not just deterministic UI flows, but AI-driven behaviour  agent responses, RAG answers, document generation  where “expected result” is a judgment call, not an assert.

You will work side by side with the AI-First Product Analyst (who writes the specs you test against) and engineering (whose agents implement them). You will use our shared Claude Team / Cowork with premium seating along with the company's AI tools daily, deeply and natively — not as a novelty, but as the default way you work.

We operate in a fast-moving startup environment where speed and precision both matter. You must be opinionated about which QA tasks AI does well today (test-case generation, coverage analysis, log triage), which it does not (exploratory judgment, UX intuition, risk feel) — and how to combine both deliberately.

What you will own & do

AI-First Way of Working

• Daily, deep, native use of AI tools for test-case generation from specs, coverage gap analysis, bug report drafting, log and session triage, regression checklist upkeep.

• Treat AI tools the way an engineer treats their IDE: opinionated, tuned, integrated into every step of your workflow.

• Contribute to internal evals from the QA side: define what “good” looks like for AI-generated code, AI-generated test artifacts and our user-facing AI features.

Manual Testing & Test Design

• Own manual testing end to end: exploratory sessions, spec & story-based test design, edge cases, negative paths, cross-role and multi-tenant scenarios.

• Convert machine-readable specs & stories with acceptance criteria into structured, traceable test cases and checklists — every requirement maps to a verification.

• Run deep exploratory testing where scripted coverage ends: real-world flows, messy data, weird devices, impatient users.

• Verify complex B2B2C surfaces: dashboards, role-based access, real-time data, payment flows, document generation, back-office tooling.

Testing AI-Driven Behaviour

• Test the non-deterministic layer: agent conversations, autonomously generated compliance documents — correctness, groundedness, tone, failure modes.

• Design and run structured evaluation of LLM outputs with the Analyst and engineering: treat AI output as something to be measured, not trusted.

• Hunt for hallucinations, prompt-injection surfaces, permission leaks across tenants and roles, and silent agent failures.

Quality Process & Release Ownership

• Own release readiness: regression scope, smoke passes, UAT coordination with stakeholders, GO/NO-GO input for every release.

• Write bug reports engineers (and agents) can act on without a follow-up question: environment, steps, expected vs actual, evidence, severity.

• Feed spec & story defects back upstream: when the work item is wrong or ambiguous, it gets fixed at the spec/story layer, not patched in QA.

• Build the QA practice from scratch: process, tooling, test data, defect workflow in Jira — lightweight enough for startup speed, rigorous enough to trust.

What we are looking for

Must have

 4+ years of hands-on manual QA experience on web products, with senior-level ownership of quality for a product or major product area.

• 0 → 1 builder: comfortable being the first and only QA, setting up the practice yourself, working directly with founders and engineering — not joining an already-scaled QA org.

• Experience testing LLM-based features, chatbots, RAG systems or agent workflows — including designing evals or output rubrics.

• Daily, deep, native use of AI tools in your QA workflow: test generation, triage, analysis, documentation. You have a real, opinionated workflow built around them and can defend your choices.

• Excellent test design craft: from acceptance criteria to test cases, edge cases, negative scenarios and risk-based prioritization — structured, traceable, unambiguous.

• Strong exploratory testing instincts: you find the bugs no script would — and can explain how you found them.

• Sharp written bug reporting and strong verbal communication in English (C1+), under startup timelines, without losing rigor.

• Comfortable with complex B2B / multi-sided product surfaces: multi-tenant SaaS, role-based access, real-time data, payments, back-office tooling.

 Working knowledge of APIs and databases for verification: reading API responses (Postman or similar), basic SQL to check data behind the UI, reading logs.

Strong advantage (Nice to have)

• Background in PropTech, hospitality tech, Fintech or multi-sided marketplace platforms.

• Exposure to payment flows, compliance / regulatory workflows or document-generation systems.

• Test automation literacy (Playwright, Cypress or similar) — enough to direct agents that write automation, even if you don't write it daily.

Why this role

You will help build the first AI-native operating system for real estate in Dubai and define what quality means when AI agents write most of the code. Both are differentiators, both are hard and both are yours to own at the senior level.

• Genuine quality ownership — you build the QA practice from zero and hold the release bar. This is not a “execute someone else's test cases” role.

• Full reservation from mobilization for male employees — officially arranged by the company, so you can focus on the work.

• Clear growth trajectory — this role evolves into AI-First QA Lead / Head of Quality as the team scales.

• Direct access to founders and Head of Delivery & Product — short decision loops, no middle management.

• Technically ambitious product surface  AI-native, Agentic system, RAG, IoT, real-time systems, government and regulatory APIs and eventually tokenization infrastructure.

 AI-first by default — you will co-design how quality assurance works inside an AI-first delivery machine.

• Great office in the centre of Kyiv — one on-site team, short feedback loops, fast decisions.