Ralabs

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.

What is this project about?

This long-term fintech project is dedicated to simplifying taxes, tax refunds, and tax incentives in the US market. Our platform seamlessly integrates software and dedicated tax professionals to optimize users’ R&D tax credit services — supporting finance and tax-related business logic that has helped businesses claim billions of dollars in tax credits and incentives. Users simply upload their data, the platform performs analyses and generates the necessary tax forms, allowing clients to maximize their benefits efficiently.

What is the team size and structure?

You will be working in close collaboration with Senior Engineers, and communicate directly with the client, Lead, and PM from our side.

Hard skills

• 5+ years of professional backend development experience;
• Deep expertise in Python programming; strong experience with FastAPI or similar frameworks;
• Solid understanding of relational databases, particularly PostgreSQL;
• Strong knowledge of software design patterns and architecture best practices;
• Experience with RESTful API design and development;
• Experience with cloud platforms (preferably Azure);
• Experience with microservices architecture and containerization (Docker);
• Familiarity with Git-based workflows, CI/CD processes, and code reviews;
• Experience integrating third-party services and APIs (e.g., Salesforce, NetSuite);
• Experience with automated testing frameworks;
• Proven ability to build scalable, performant, and secure backend applications;
• Upper-Intermediate English level or higher.
   

Responsibilities

• Design, build, and maintain efficient, reusable, and reliable backend code in Python;
• Develop RESTful APIs and backend services integral to the Unified Partner Portal;
• Ensure seamless integration with platforms like Salesforce and NetSuite;
• Collaborate with front-end developers, UI/UX designers, product owners, and stakeholders to translate requirements into technical solutions;
• Develop rigorous automated tests to ensure system reliability and robustness;
• Optimize API performance and database queries for scalability, security, and maintainability;
• Monitor application performance and troubleshoot issues using observability tools (e.g., Datadog);
• Mentor and provide technical guidance to junior and mid-level developers;
• Document technical designs and processes clearly and comprehensively.
  
  How many stages of the interview are there?
• Interview with the Recruiter – up to 30 min.;
• Tech interview on our side – up to 1 hour.;
• Tech interview on our client side – up to 1 hour.

We are looking for a Senior Data Engineer to join a project in the tech capital of the world – Silicon Valley.

Working hours are typically until 7–8 PM Kyiv time, plus a late call twice a month around 11 PM.
 

Project Idea

The project was founded back in 2014 with the goal of connecting private and government universities 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?

Must have:

• 5 + years of experience in Data Engineering or related roles;
• Strong SQL skills and hands-on experience with PostgreSQL (including Aurora Serverless);
• Solid knowledge of Python for data processing and automation;
• Experience building and maintaining ETL/ELT pipelines using cloud-native tools (e.g., AWS Lambda, S3, SQS);
• Proven experience working with MongoDB and MongoDB Atlas, including event-driven architectures using Atlas Triggers, Stream Processing;
• Proficiency with dbt for building modular, testable, and well-documented data transformation workflows;
• Good understanding of data modeling principles for OLAP/OLTP systems, including normalization and dimensional modeling;
• Demonstrated experience designing and implementing data warehouses and data marts;
• Working knowledge of Node.js, particularly in backend logic tied to data ingestion or transformation workflows;
• Familiarity with cloud data platforms (e.g., AWS) and serverless computing patterns;
• A technical degree (e.g., Computer Science, Engineering, Math) is a plus;
• Upper-Intermediate English or higher for effective communication and documentation.

Soft skills:

• Proactive – you take ownership and act without waiting for direction;
• Detail-oriented – you deliver accurate, high-quality work;
• Initiative-driven – you’re eager to improve processes and take action.

Responsibilities:

• Design, implement, and maintain scalable and reliable data pipelines using Python, dbt, and AWS Lambda;
• Build and optimize data architectures to support analytics, reporting, and machine learning use cases, including data warehouse and data mart modeling on PostgreSQL (Aurora Serverless);
• Develop and manage ELT workflows that extract data from MongoDB (using Atlas triggers) and load into staging and production layers in PostgreSQL;
• Ensure data consistency and lineage by applying robust data quality checks, auditing, and reconciliation logic;
• Collaborate with cross-functional teams to gather data requirements, understand business logic, and translate them into efficient data models and transformations;
• Monitor and troubleshoot SQS/Lambda-based ingestion pipelines, addressing issues related to concurrency, message processing, and data duplication;
• Contribute to the semantic layer design used by BI and reporting tools to ensure consistency and accessibility of business metrics;
• Maintain and evolve dbt models (staging, intermediate, and marts) aligned with software engineering and analytics best practices;
• Drive continuous improvement in data engineering processes and data governance standards, ensuring scalability, maintainability, and security.

Hello everyone!
We’re looking for a hands-on QA Lead to manage quality practices for our project. This role combines people/process leadership with strong practical expertise in both Manual QA and Automation QA, with automation built using our Playwright-based (JS) framework.
 

Project Idea

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?
 

What exciting things is the product doing for the community?

The product is transforming the way public and private spaces are discovered, reserved, and used across communities — all online and accessible anywhere.
 

What is the team size and structure?

Our team consists of: 2 Front-End developers, 3 Back-End developers, 1 React-Native developer, 2 UI/UX Designers, 2 QA, 3 AQA, and PM.

How many stages of the interview are there?

— Interview with the Recruiter — up to 30 min.;

— Technical interview — up to 1.5 hours;

— Interview with the client — up to 1 hour.
 

Key Responsibilities:

• Own and drive the overall QA strategy across the project (manual and automation), defining quality standards, release readiness criteria, and continuous improvement initiatives aligned with product risk and delivery cadence.

  ‼️ Work with a required daily overlap of ~6 hours with the client’s time zone; this typically means working until approximately 9 PM — 12 AM EET.

• Lead hands-on test planning and execution for features and epics, providing early feedback on requirements to improve clarity, testability, and quality.
• Maintain a hands-on balance between leadership and execution, with approximately 60% focused on QA leadership and strategy, and 40% dedicated to hands-on testing and automation.
• Ensure efficient, lightweight test documentation, strong regression coverage, and clear traceability between requirements, tests, and defects.
• Own, maintain, and evolve test automation using the Playwright-based JS framework, expanding reliable coverage for critical user flows.
• Proactively manage flaky tests, improve test stability, and ensure automation is trusted by the team.
• Partner closely with Engineering and Product teams to establish shared ownership of quality, run bug triage, align on defect severity and priorities, and support confident release decisions.
• Mentor QA and AQA engineers through coaching, reviews, and knowledge sharing; help define clear QA ownership within the team.
• Contribute to hiring, onboarding, and shaping QA best practices when needed.

Requirements:

Must have:

• 5+ years in Quality Assurance with a mix of manual and automated testing.
• Prior experience in a QA Lead role with clear ownership of QA processes within a team.
• Solid hands-on experience with test automation using Playwright framework.
• Strong understanding of test strategy, test planning, risk-based testing, and release readiness.
• Ability to mentor, coach, and review the work of other QA engineers.
• Strong communication skills and a proactive, ownership-driven mindset.
• Upper-Intermediate English or higher for effective communication and documentation.

Nice to Have:

• Experience with API testing (Postman and/or automated API tests).
• Basic knowledge of SQL and experience debugging web applications using browser developer tools.
• Experience with CI/CD integration (GitHub Actions, CircleCI, Jenkins).
• Familiarity with test reporting tools (Allure, HTML reports) and test management tools.
• Exposure to performance, accessibility, or security testing fundamentals.
• Prior experience building or heavily refactoring a test automation framework, including architecture decisions, patterns, and stability improvement.

What Success Looks Like (first 3-6 months):

• Clear QA operating model is in place: test strategy, quality gates, regression approach, and triage routine.
• Automated suite coverage increases for critical paths, and flakiness decreases with a stable CI signal.
• Fewer escaped defects and better predictability for releases.
• The team demonstrates stronger testing habits: better AC clarity, earlier risk identification, and consistent QA standards.