Ralabs

We are looking for a highly experienced Technical Product Manager to join our exciting long-term project!

Note: This role has shifted hours, running until 3:00 AM Kyiv time (5:00 PM PDT), to overlap with our international team.

 

About the Project

The project was founded back in 2014 with the goal of connecting private and government universities/schools with regular people like we are. You have a variety of auditoriums, gyms, classrooms, and other venue options available for community use -
schedule facility uses and manage requests from the community all in one place.

Just imagine that you’re a football player and you can rent a football field at Harvard to play with your friends. Amazing, right?

 

About the Role

This role bridges product and engineering, translating feature requests and PRDs into clear, actionable technical plans. You’ll work across a Node.js/AWS product line focused on scheduling and rentals, helping the development team stay focused on building by handling technical decomposition, documentation, and cross-team coordination.

Hiring Process

• Interview with the Recruiter — up to 30 min.
• Interview with the PM — up to 30 min.
• Tech interview on our side — up to 1 hour.
• Tech interview on our client side — up to 1 hour.

What You’ll Do:

• Technical Requirements

Turn feature requests and PRDs into clear, development-ready Jira stories. Work with Product Managers to assess feasibility and choose the best implementation approach. Break down complex initiatives into well-scoped work items with dependencies and edge cases clearly documented.

• Engineering Support

Do the upfront research — business context, data models, API contracts — so engineers spend less time gathering background. Act as the main link between Product and Engineering, keeping priorities and tradeoffs clear on both sides, and represent engineering in meetings without overloading their schedules.

• Ticket & Workflow Management

Keep Jira tickets clean, clear, and actionable before they hit the sprint backlog. Link tickets to relevant PRDs, designs, and technical docs. Make sure acceptance criteria are testable and aligned with QA.

• Documentation & Process

Build and maintain templates for technical specs, PRDs, and ticket formats. Help refine the workflow from PRD to release. Document architecture decisions, API designs, and integration patterns for the team’s shared knowledge base.

• Feature Development

Explore the existing codebase to break down new features and present implementation options with tradeoffs. Author technical design docs that serve as an engineering blueprint. Flag technical debt and modernization opportunities alongside new feature work.
 

• AI & Emerging Tech

Explore where LLMs and agentic AI can improve development workflows and product features — from AI-assisted coding to smarter user-facing tools. Prototype AI-driven solutions and help set technical standards for AI/ML work across the platform.

• Release Management

Help keep releases smooth and well-coordinated. Create release notes, walkthroughs, and change summaries. Monitor post-release metrics and support bug triage using session data and support tickets.

 

What You Bring

• 5+ years in product management, with prior hands-on experience in a technical role (preferably software engineering), and strong ability to coordinate across Product and Engineering
• Strong technical aptitude — able to read and reason about code, APIs, data models, and system architecture across multiple stacks
• Hands-on experience turning business requirements into detailed technical specs and engineering-ready stories
• English — B2 or higher
• Proficiency with Jira and Confluence
• Excellent written communication — clear, structured documentation engineers can act on independently
• Experience with Agile/Scrum, including sprint planning, estimation, and release management
• Understanding of LLMs and agentic AI product development

Key Deliverables

• Clear, actionable technical requirements ready for engineering
• Well-prepared Jira tickets with acceptance criteria and linked docs
• Technical design documents that guide implementation
• Streamlined, standardized release processes
• Less time spent by engineers on research and context-gathering
• Reusable templates and documentation standards

What is this project about?

An AI-powered analytics platform built for the defense domain. It ingests and analyzes data from multiple sources, automatically surfacing key findings and generating actionable insights and recommendations. By automating heavy manual analysis, the platform helps organizations make faster, better-informed decisions, reduce manual effort, and improve overall operational performance.

What is the team size and structure?

Project Manager, Backend and ML Engineers, UI/UX Designer, DevOps.

Hard skills

● 7+ years of experience in Software Engineering;
● Strong experience designing complex distributed systems and service-oriented architectures;
● Hands-on experience with AI/ML system architecture, including LLM-based systems, inference layers, vector search, and data pipelines;
● Solid understanding of LLM reasoning, tool calling, agent orchestration, and human-in-the-loop workflows;
● Ability to decompose complex platforms into services, modules, and integration layers, and to define clear boundaries between LLM, ML, deterministic logic, and human validation;
● Strong understanding of data architecture: data lakehouse, vector databases, object storage, and federated queries;
● Experience designing for scalability, latency, throughput, and fault tolerance;
● Experience designing secure systems for sensitive or critical data, with awareness of AI security risks (prompt injection, unsafe tool execution, data leakage, runtime protection);
● Strong documentation skills: architecture diagrams, ADRs, technical specifications, and integration flows;
● Ability to work with incomplete information, technical uncertainty, and evolving requirements;
● High level of ownership, technical leadership, and proactive decision-making;
● Upper-Intermediate level of English.

Responsibilities

● Design end-to-end system architecture for the AI-based data analysis platform;
● Decompose the system into services: agent runtime, orchestration, inference layer, data layer, security layer, and integrations;
● Define boundaries between LLM reasoning, ML processing, deterministic rules, and human decision-making;
● Design architecture for AI recommendations, next-best-action logic, semantic search, and data analysis workflows;
● Design a scalable inference architecture and model-serving approach;
● Define latency, throughput, fault-tolerance, and scaling strategy from single-node to cluster deployment;
● Design data architecture for ingestion, storage, cataloging, vector search, querying, and access control;
● Define the orchestration approach for long-running AI/agent workflows;
● Establish security, auditability, runtime protection, and data leakage prevention principles;
● Collaborate with engineering, DevOps, ML/data engineers, product, and business stakeholders;
● Support technical discovery, feasibility analysis, POC creation, concept validation, estimation, and roadmap planning;
● Identify architectural risks, dependencies, constraints, and mitigation options;
● Prepare and maintain architecture documentation, diagrams, ADRs, and technical recommendations;
● Guide engineering teams on implementation approach, scalability, performance, and architectural standards.

Nice to have

● Experience in the Defense/MilTech and AI domains;
● Experience with on-premise/hybrid infrastructure.

How many stages of the interview are there?
● Recruiter interview – up to 30 minutes;
● Technical interview – up to 1.5 hours;
● Polygraph screening.