Scaling an AI Job Search Platform with Automation, Observability, and DevOps

As the product gained traction, JobHire.AI needed to move beyond the typical early-stage startup setup. The platform was already generating revenue and serving users, but its infrastructure, development workflows, testing processes, and internal operations needed to become more secure, automated, and scalable.

Singula team joined with DevOps, development, and QA support. The work ranged from closing exposed infrastructure and automating test environments to improving the acquisition funnel, reducing billing setup from 1.5–2 hours to about 5 minutes, and testing the web and mobile experience before issues reached users.

The result was a more secure, observable, and efficient technical environment where the team could release, test, and iterate faster.

About JobHire.AI

JobHire.AI is built for job seekers who want to spend less time manually applying for jobs and more time preparing for interviews. The platform focuses on the US market and automates key parts of the job search journey, including job matching, application submission, resume optimization, cover letter generation, email generation, and activity tracking.
According to the company’s website, JobHire.AI processes more than 1,000,000 jobs every month, helps users save up to 40 hours per week with its auto-apply feature, and has supported more than 35,000 job seekers.
For users, the value is simple: fewer repetitive tasks and more opportunities. For the engineering team, however, this creates a complex technical challenge. The platform has to support frequent experiments, payment flows, user onboarding, job application logic, analytics, internal tools, and a smooth experience across web and mobile.

The Challenge

JobHire.AI had already passed the fragile “will this product work?” stage. Users were paying, the product had momentum, and the team was moving quickly.

The problem was that the technical foundation still looked like an early startup setup.

Developers had handled most of the infrastructure themselves. They were capable engineers, but infrastructure was not their main job. Some resources were too exposed, access was managed manually, automation was limited, and the team did not yet have enough visibility into what was happening inside the system. Server logs also showed regular access attempts from outside, so security could not wait.

The team needed to:

• move infrastructure into a protected environment;
• reduce manual server setup and deployment work;
• create reliable QA environments;
• improve monitoring, logging, and incident response;
• centralize access and credential management;
• automate repetitive pricing and billing setup;
• support frequent conversion-focused product experiments;
• keep the web and mobile experience stable.

That was the real challenge: JobHire.AI needed stronger engineering foundations without slowing down the product work that made the company grow.

Singula team’s Role

We supported JobHire.AI across three areas:

DevOps
Infrastructure security, automation, monitoring, logging, access management, backups, multi-cloud setup, deployment support

Development
Frontend/backend development, acquisition and payment funnel improvements, pricing automation, social login, experiment support

QA
Manual testing of the web application across desktop and mobile browsers, including key user flows, design checks, bug reports, test cases, checklists, and test documentation

The value was not in any one role alone; JobHire.AI needed infrastructure that engineers could trust, product changes that could be tested quickly, and QA coverage that kept the funnel stable while the team kept experimenting.

Strengthening Infrastructure Security

The first change was simple in principle and painful in practice: remove direct exposure.

Parts of the infrastructure were still reachable from the public internet. That had to go. Our DevOps engineer rebuilt the network so core services sat behind a closed perimeter. Access now goes through controlled entry points, not open endpoints.

Databases followed the same path. PostgreSQL and MongoDB were moved into the protected environment with close to zero downtime. Services that couldn’t be fully relocated were locked down through allowlists.

What changed in concrete terms:

• core services moved into a closed network;
• direct public access removed;
• databases isolated inside the protected environment;
• access rights centralized and made traceable;
• credentials stored in Vaultwarden instead of scattered across tools;
• GitLab and YouTrack upgraded to eliminate known vulnerabilities;
• backups and recovery automated.

Building a More Efficient Multi-Cloud Setup

After the security work, the next question was cost.

Our DevOps engineer audited the existing infrastructure, split it into logical parts, and rebuilt it as a multi-cloud setup connected through a unified VPN network. Developers could still reach the resources they needed, but those resources no longer had to sit in the open internet.

This also gave the team more room to optimize where workloads ran. By moving parts of the infrastructure across regions and providers, JobHire.AI expected to reduce infrastructure costs by about 30%.

Automating Server Setup and Test Environments

Server setup used to take too much manual work. Engineers had to prepare environments by hand, which slowed the team down and made every setup slightly harder to trust.

Singula team replaced that with dynamic configuration. The team now defines the needed values in code, and automation handles the rest.

The difference was immediate:

• a full test environment can be prepared in about 1 hour;
• server configuration takes around 15 minutes;
• QA no longer depends on one shared test environment;
• developers and testers can work in parallel.

For JobHire.AI, this mattered because the product team runs frequent changes across the funnel. Faster environments meant fewer blockers, quicker testing, and less time lost to setup work.

Improving Monitoring, Logging, and Incident Response

The team also needed to see problems before users reported them.

Singula team set up monitoring for disk usage, CPU, RAM, service availability, and application errors. The logging layer was built on ELK, with Elasticsearch, Logstash, and Kibana, while Grafana and Prometheus helped track infrastructure health.

The setup included:

• ELK for log collection and analysis;
• Grafana and Prometheus for infrastructure monitoring;
• PostgreSQL, MongoDB, and Blackbox exporters;
• custom alerts for infrastructure metrics and application logs.

This gave the team visibility into failed login flows, regional availability issues, repeated application errors, and overloaded services. Before, incidents were harder to trace. Now the team could see where the system was struggling and respond faster.

Centralizing Access and Credentials

Access had also outgrown the old process. Before, permissions were issued manually, which made it harder to see who had access to which systems and when that access had been granted. Singula team moved access management into a single repository, so permissions became easier to review, update, and audit.
The team also introduced Vaultwarden for credential storage. This gave JobHire.AI:

• clearer access visibility;
• safer credential management;
• simpler onboarding and offboarding;
• better control over database and infrastructure permissions.

For a distributed team of around 40 people, this removed guesswork from a sensitive part of operations.

Supporting Analytics Infrastructure

Singula team also prepared the infrastructure for ClickHouse, Metabase, and Airflow. The client’s analytics specialists owned the reporting layer, but the tools needed a stable environment first. For a product built around funnel experiments, that foundation matters: without reliable data on where users register, pay, drop off, or respond to offers, product decisions become educated guesses.

Improving the Acquisition and Payment Funnel

On the development side, Singula team worked on the part of the product responsible for customer acquisition. Our developer joined the project to strengthen the team working on the user journey from the first visit through registration, payment, and transition into the account.
This part of the product is commercially critical because it directly affects conversion. 
The team worked with metrics such as:

• Customer acquisition cost
• Click-to-registration conversion
• Registration-to-subscription conversion
• Advertising performance after users entered the funnel
• User behavior during registration and payment

The process was experiment-driven. Product managers brought hypotheses, the team implemented variants for specific user segments, results were measured, and the better-performing option was kept.

Automating Pricing and Billing Configuration

JobHire.AI regularly tests subscription terms, tariffs, add-on services, discounts, promotions, and special offers. Before automation, each change had to be created, configured, and added to internal databases by hand. With several test environments, the same work often had to be repeated more than once.

That was slow. It was also risky, because billing errors can affect both revenue and user trust.

Singula team automated the process with scripts. A setup task that used to take 1.5 to 2 hours now takes about 5 minutes. For the business, this meant faster pricing tests and fewer chances for human error in a part of the product where mistakes are expensive.

Reducing Friction with Social Login

We also worked on user-facing improvements, including Google-based authorization and registration.

For JobHire.AI, registration is part of the revenue-critical funnel. Any unnecessary friction can affect conversion. Social login helps users start faster, reduces the need to create another password, and supports a smoother onboarding experience.

It is a small product feature with a clear business purpose: help more users move from interest to account creation and payment.

Using AI in the Development Workflow

JobHire.AI is an AI-first product, and the company also encourages AI use inside the development process. Our engineering team uses AI-assisted tools for task analysis, codebase exploration, draft solution design, and implementation support. The developer described a workflow where AI helps analyze tasks, clarify requirements, search through the codebase, propose a solution design, and assist with code generation under developer supervision.

The team uses tools such as Codex, Claude Code, and Copilot. This did not replace engineering judgment. It helped with the early shape of the work: reading tasks, searching the codebase, drafting possible solutions. Developers still reviewed the logic and made the final calls.

QA: Protecting UX, Conversion, and Retention

Singula Team’s QA engineer focused on manual testing of the web application across desktop and mobile browsers. Testing covered both visual accuracy and functional scenarios, including authorization, registration, purchase flows, and other user actions that directly affect conversion.

The QA scope included:

• testing UI functionality across different devices and browsers;
• checking implementation against design mockups;
• finding non-obvious usability issues;
• creating clear bug reports;
• maintaining test cases and checklists;
• describing scenarios for automated tests.

This work helped prevent critical errors, improve usability, and protect conversion and retention. It also gave the client’s internal team a clearer view of the product’s current state, defect patterns, and testing coverage, so product managers and business analysts could spend less time clarifying issues and more time planning future improvements.

Technology Stack

Frontend
TypeScript, React, Next.js, TanStack Query, Zustand, Framer Motion, Storybook, Jest, Playwright

Backend
TypeScript, NestJS, TypeORM, PostgreSQL, RabbitMQ, Elasticsearch, Jest

DevOps and Infrastructure
Linux, GitLab CI, Ansible, Terraform, ELK, Grafana, Prometheus, PostgreSQL, MongoDB, ClickHouse, Airflow, Metabase, Hetzner Cloud, NetBird, Vaultwarden, Nginx, RabbitMQ

Results

The biggest result was control. JobHire.AI’s team could keep experimenting without relying on fragile manual work or exposed infrastructure. Security became less reactive. Testing became less dependent on one shared environment. Billing changes stopped taking hours. Incidents became easier to trace. Access became easier to audit.

The numbers show the shift clearly. Test environments can now be prepared in about 1 hour. Server configuration takes around 15 minutes. Pricing and billing setup dropped from 1.5–2 hours to about 5 minutes. Infrastructure changes are also expected to reduce costs by around 30%.

For the product team, this meant fewer blockers between an idea and a tested release. For QA, it meant more stable conditions for checking the web and mobile experience. For the business, it meant the platform could keep moving quickly without carrying as much operational risk.

Conclusion

JobHire.AI had already proved there was demand for its product. The next step was making the platform safer, faster to change, and easier to operate.

Singula team helped close that gap across infrastructure, development, and QA. The work reduced operational risk, removed repetitive manual tasks, gave the team better visibility into the system, and made testing more predictable.

For JobHire.AI, that means fewer technical distractions around the product and more room to keep improving the job search experience.

Need a Team for a Project Like This?

If your product is growing faster than your infrastructure, testing process, or internal tooling can support, Singula team can help. We bring DevOps, development, QA, and automation specialists into active products that need to keep shipping while the foundation gets stronger.

Whether you need DevOps, frontend or backend development, QA, automation, or a cross-functional team for a fast-moving product, we can join your project and help you move faster with more confidence.

Hire Singula team for your next project!