Hiring: AI-Native Technical Product Owner & Agentic Systems Engineer

Company: RoamYoo
Employment Type: Full-Time
Experience: 3–7 years preferred; exceptional builders with demonstrable production experience are welcome

About RoamYoo

RoamYoo is building an AI-powered, social-meets-marketplace travel platform that connects travellers with verified local hosts, authentic experiences, group trips, stays, transport, and community-driven travel opportunities.

We are looking for a highly capable, technology-driven professional who can take ownership of multiple applications, manage the complete product lifecycle, build AI-powered systems, automate engineering workflows, and ensure that every application is reliable, secure, scalable, and production-ready.

This is not a conventional Product Owner role focused on meetings, documentation, and task management.

We need a hands-on technical owner who can think like a product manager, build like an engineer, automate like a DevOps specialist, and troubleshoot like a production engineer.

Role Overview

You will own the complete lifecycle of multiple digital products and applications, from requirements and architecture to development, testing, deployment, monitoring, and continuous improvement.

You must understand how contemporary AI coding tools, agentic workflows, APIs, databases, cloud infrastructure, CI/CD pipelines, and automation systems work together to deliver production-grade applications.

You will be expected to work independently, identify technical and product problems, implement solutions, coordinate with other developers when required, and take accountability for outcomes rather than simply completing assigned tasks.

Key Responsibilities1. Product Ownership & Application Management

- Own multiple web applications, mobile applications, internal tools, admin panels, and AI-powered products.
- Convert business requirements into product specifications, user stories, technical tasks, and acceptance criteria.
- Manage product roadmaps, sprint priorities, backlogs, releases, and dependencies.
- Understand user journeys, business logic, database relationships, API integrations, and application architecture.
- Coordinate product development from initial prototype to MVP and production release.
- Identify bottlenecks, technical debt, performance issues, and opportunities for automation.
- Maintain product documentation, system diagrams, API documentation, release notes, and operational runbooks.
- Ensure applications remain functional as requirements, users, integrations, and business processes evolve.

2. Agentic AI Systems & AI Engineering

- Design, implement, and maintain agentic AI workflows that can plan, execute tasks, use tools, and validate outcomes.
- Build multi-agent systems where appropriate, including orchestration, delegation, state management, retries, and human approval checkpoints.
- Work with LLM APIs, structured outputs, function calling, tool use, retrieval-augmented generation (RAG), and embeddings.
- Understand agent memory, context management, evaluation, guardrails, and failure recovery.
- Integrate AI agents with databases, internal applications, third-party APIs, browser tools, and business workflows.
- Develop reliable workflows rather than depending on unstructured prompts alone.
- Implement observability, tracing, evaluation datasets, token and latency monitoring, and cost controls.
- Know when to use a deterministic workflow instead of an autonomous agent.

Relevant technologies: OpenAI APIs, Anthropic APIs, LangGraph, LlamaIndex, LangChain, MCP, vector databases, and other relevant agent orchestration frameworks.

3. AI-Assisted & Agentic Software Development

- Work effectively with Claude Code, OpenAI Codex, Cursor, VS Code, and other AI-assisted development environments.
- Use AI coding agents for implementation, debugging, refactoring, test generation, code review, and documentation.
- Create reusable project instructions, coding standards, agent skills, tool integrations, and repeatable development workflows.
- Design multi-step coding workflows with planning, implementation, testing, review, and validation.
- Understand the code being generated rather than blindly accepting AI-generated output.
- Review AI-generated code for security vulnerabilities, incorrect business logic, performance problems, and maintainability.
- Use Git-based version control, pull requests, branch protection, and code review practices.
- Establish a repeatable workflow for moving from an idea or prompt to tested, deployable software.

4. Full-Stack Development & System Architecture

- Understand and work across frontend, backend, databases, APIs, authentication, infrastructure, and integrations.
- Build and maintain responsive, accessible, and performant user interfaces.




- Design REST APIs and understand GraphQL where appropriate.
- Implement authentication, role-based access control, permissions, session management, and secure data handling.
- Design database schemas, migrations, indexes, relationships, transactions, and query optimization.
- Integrate payment gateways, messaging systems, maps, analytics, AI services, and third-party platforms.
- Understand modular monoliths, microservices, event-driven systems, background jobs, and asynchronous processing.
- Diagnose issues across the entire stack instead of treating frontend and backend as isolated components.

Preferred technologies: TypeScript, JavaScript, React, Next.js, Node.js, Python, FastAPI, PostgreSQL, Redis, Supabase, and other equivalent production-grade technologies.

5. Automated Deployment & DevOps

- Build and maintain CI/CD pipelines for automated testing, builds, deployments, and release verification.
- Configure development, staging, and production environments.
- Manage environment variables, secrets, configuration, and database migrations safely.
- Work with GitHub Actions, Docker, cloud hosting, managed databases, and deployment platforms.
- Automate build validation, linting, type checks, test execution, security scanning, and release workflows.
- Implement deployment verification, health checks, rollback procedures, and recovery plans.
- Understand containerization, infrastructure configuration, networking, logs, and application runtime environments.
- Manage production releases with minimal downtime and controlled risk.
- Troubleshoot failed deployments, dependency conflicts, build errors, server issues, and environment-specific bugs.

Relevant tools: GitHub Actions, Docker, Vercel, AWS, GCP, Azure, Railway, Render, Supabase, and Terraform or equivalent infrastructure-as-code tools.

6. Quality Assurance & Production Readiness

- Build a reliable testing strategy covering unit, integration, API, end-to-end, and regression testing.
- Use tools such as Playwright, Vitest, Jest, or equivalent frameworks.
- Verify critical business flows before releases, including authentication, payments, bookings, notifications, and admin operations.
- Identify race conditions, edge cases, inconsistent data, permission errors, and failure scenarios.
- Implement structured logging, error tracking, uptime monitoring, and application performance monitoring.
- Use tools such as Sentry, OpenTelemetry, and cloud-native monitoring solutions.
- Establish production-readiness checklists, incident response procedures, backup policies, and disaster recovery practices.
- Ensure that an application is not considered complete merely because it works on a developer's machine.

7. Security, Reliability & Scalability

- Apply secure coding practices and follow relevant OWASP guidance.
- Prevent common vulnerabilities such as injection, broken access control, exposed secrets, and insecure API endpoints.
- Protect user data and implement appropriate authorization, validation, rate limiting, and audit logging.
- Understand secure authentication flows, password handling, OAuth, and token management.
- Manage database backups, recovery procedures, dependency vulnerabilities, and access permissions.
- Identify scalability bottlenecks and optimize database queries, API response times, caching, and background processing.
- Establish sensible controls for AI tool permissions, prompt injection, sensitive data exposure, and unsafe agent actions.

8. Technical Documentation & Engineering Operations

- Maintain a clear inventory of all applications, repositories, environments, domains, integrations, and deployment pipelines.
- Document architecture, setup instructions, environment configuration, dependencies, and troubleshooting procedures.
- Track technical debt, recurring production issues, service dependencies, and infrastructure costs.
- Create reusable templates and standards so new applications can be launched consistently.
- Ensure that no critical system depends entirely on undocumented knowledge held by one individual.

Required Technical Skills

Candidates must be able to demonstrate practical competence in the following areas:

- Product engineering: Requirements gathering, feature prioritization, user stories, acceptance criteria, release planning, and product lifecycle ownership.
- Programming: Strong proficiency in at least one production-grade language, preferably TypeScript or Python.




- Full-stack fundamentals: Frontend, backend, REST APIs, authentication, databases, and integrations.
- Agentic AI: LLM integration, tool calling, agent orchestration, structured outputs, workflow reliability, and evaluation.
- AI development tools: Practical experience with Claude Code, Codex, Cursor, or comparable AI coding agents.
- Version control: Git, GitHub, branching strategies, pull requests, and code review.
- CI/CD: Automated pipelines, build checks, deployment automation, release verification, and rollback.
- Cloud and infrastructure: Practical experience deploying and operating real applications.
- Testing: Automated unit, integration, and end-to-end testing.
- Production operations: Logs, monitoring, incident diagnosis, backups, performance, and reliability.
- Security: Secrets management, authorization, secure API design, and dependency management.
- Technical communication: Ability to explain architecture, trade-offs, risks, and implementation decisions clearly.

Preferred Qualifications

- Experience managing or maintaining multiple production applications.
- Experience building and deploying AI-powered products or agentic workflows.
- Familiarity with MCP servers, reusable agent skills, and tool integrations.
- Experience with Supabase, PostgreSQL, authentication systems, and database migrations.
- Experience with payment gateways, third-party integrations, and webhook processing.
- Understanding of queues, scheduled jobs, event-driven workflows, and distributed system fundamentals.
- Experience with observability, cloud cost optimization, and infrastructure automation.
- Familiarity with travel technology, marketplaces, booking systems, SaaS products, or consumer applications.
- Experience working in a startup environment with limited resources and high ownership expectations.

What We Expect From You

- Take ownership from problem identification to verified resolution.
- Build and maintain systems instead of relying entirely on other developers or external agencies.
- Make informed decisions about when to code manually, use AI agents, or automate a workflow.
- Prioritize reliability, security, maintainability, and business value over impressive demos.
- Move quickly without compromising essential engineering standards.
- Work independently, communicate blockers early, and document critical decisions.
- Be comfortable operating in an environment where product requirements and priorities evolve.
- Treat the product as something you are responsible for building and improving, not merely a list of assigned tickets.

First 90-Day Success Criteria

First 30 days — Understand and stabilize

- Audit existing applications, repositories, infrastructure, integrations, and deployment processes.
- Identify critical bugs, security gaps, undocumented dependencies, and operational risks.
- Create an application inventory, architecture overview, and prioritized improvement roadmap.

Days 31–60 — Automate and improve

- Establish reliable CI/CD workflows for priority applications.
- Improve automated testing, monitoring, error reporting, and release procedures.
- Build or improve at least one meaningful AI-powered workflow or agentic automation.
- Document setup, deployment, rollback, and recovery processes.

Days 61–90 — Deliver and scale

- Independently deliver a production-ready feature or application improvement.
- Demonstrate repeatable, tested deployments with appropriate release safeguards.
- Establish measurable engineering KPIs, including deployment success, escaped defects, recovery time, and release lead time.
- Present a technical roadmap covering scalability, security, AI automation, reliability, and engineering efficiency.

Application Process

Please share:

- Your resume and LinkedIn profile.
- GitHub profile, portfolio, or relevant technical projects.
- One application you have taken from development to production.
- One example of an AI agent or automation workflow you have built.
- Your role in deployment automation, testing, monitoring, and production troubleshooting.
- The AI coding tools, frameworks, cloud services, and development tools you use regularly.

Candidates may be asked to complete a practical assessment covering application architecture, AI-assisted development, agentic workflows, debugging, automated deployment, and production-readiness decisions.

We are looking for builders who can take complete ownership of technology and turn ideas into dependable, production-ready products.

Pay: ₹519,000.00 - ₹630,000.00 per year

Application Question(s):

- What kind of end to product you have handled
- Define product ownership to you, as core skills and expertise you should have.
- Define product ownership role to you, as core skills and expertise you dont have.

Work Location: Remote

📌 Technical Product Owner (Delhi)
🏢 Vaanee AI Labs
📍 Delhi