Ref Id: HREZ-14671
Published Date: Jul 21, 2026
Job location: Remote, Latin America
General information
Description
HATCHWORKS AI Forward Deployed Engineer Client-Embedded · Java / Angular / AWS · Microservices · Remote / LatAm |
Remote / LatAm | Full-Time | English Required | Client-Embedded |
THE ROLE
Some environments have complexity as a feature. This one does.
The client runs a microservices architecture spanning more than 1,700 repositories. A system where finding the right place to intervene is itself a skill. The Forward Deployed Engineer embeds directly inside this environment, learns how it actually works, identifies where AI creates real leverage, and builds and ships the solution — end to end, with full accountability for what makes it to production.
This is not an advisory seat. You are not here to produce a recommendation deck or a proof of concept. You embed, scope, build, deploy, and own the outcome through adoption — operating largely standalone with light oversight rather than a dedicated product manager managing your backlog.
The stack is Java-first on the backend, Angular on the frontend, PostgreSQL for data, and AWS for cloud. AI developer tooling — Cursor, Claude, and GitHub Copilot — is already in active use by the client's engineering team. You will be working in an AI-native engineering environment from day one.
1,700+ repositories. Your job is to know which handful are worth touching — then build something that runs.
CLIENT TECH STACK
Client Tech Stack |
Backend · Java (primary) · Python (secondary) |
Frontend · Angular |
Database · PostgreSQL |
Cloud · AWS |
Architecture · Microservices — 1,700+ repositories |
AI Dev Tooling · Cursor · Claude · GitHub Copilot |
Infrastructure · Docker · REST APIs · CI/CD pipelines |
WHAT YOU'LL ACTUALLY DO
- Embed with the client's engineering and business teams, map their architecture and workflows, and identify where AI creates the highest leverage across a 1,700+ microservice environment — finding the right place to intervene is half the job.
- Scope, design, build, and ship AI-powered solutions end-to-end — from the Java service layer through the Angular frontend and into the AWS infrastructure, without destabilizing a complex distributed system.
- Write production-grade Java and Python, integrating AI capabilities into existing services, REST APIs, and data pipelines. You own the build and the deploy; the client's platform team handles infrastructure provisioning.
- Navigate a large-scale microservices architecture — know when to build a new service, when to extend an existing one, when to wire through an API boundary, and when to stay out of a system entirely.
- Use AI developer tooling as a force multiplier: Cursor, Claude, and GitHub Copilot are expected tools here, not new introductions. Your job is to use them more strategically than anyone else in the room.
- Deploy and operate solutions in AWS using Docker and CI/CD pipelines, with full accountability for reliability and adoption — not just go-live.
- Translate between AI capability and business reality with engineering leadership and business stakeholders, holding your own at both levels.
- Codify what works — turn repeatable patterns into accelerators and playbooks that make the next engagement faster for the whole HatchWorks AI team.
DEFINITION OF SUCCESS
Success is a working solution running in production, within the client's existing microservices infrastructure, that creates measurable impact on the outcome it targets. Not a prototype. Not a sandbox deployment. Not a demo. A solution the client's team uses and can operate without ongoing dependence on the engineer.
Definition of Success |
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THE TECHNICAL BAR
We set a firm minimum on technical depth. The path you took to get here is open — what matters is that you can own a full build inside a complex distributed system and ship it to production.
Must-Haves | |
Java (Production) | Strong production Java engineering — able to build, extend, and integrate Java services confidently in a complex microservices environment. Familiarity with Spring Boot or comparable enterprise Java frameworks is expected |
Full-Stack Range | Frontend proficiency in Angular; owns the full feature from backend service through UI. A backend-only specialist is not a fit — this engagement touches every layer |
Microservices at Scale | Experience navigating and extending distributed systems at scale; understands service boundaries, API contracts, and the failure modes of large microservices architectures. 1,700 repos is not a spec for a single-stack engineer |
Cloud & CI/CD | AWS deployment experience with Docker and CI/CD pipelines in a production context; able to ship and maintain solutions without relying on a DevOps team to carry the load |
PostgreSQL & Data | Relational data modeling, schema design, and query optimization in PostgreSQL; builds data layers that perform at scale and integrate cleanly with microservices |
AI Integration | Hands-on experience integrating LLMs or AI capabilities into production Java or Python services — not just experimenting. Understands how to design prompts, retrieval pipelines, and agent workflows that work reliably in production |
AI Developer Tooling | Active daily user of Cursor, Claude, or GitHub Copilot; treats these as engineering multipliers and uses them to move faster, reason better, and write cleaner code — not as autocomplete |
Python | Secondary proficiency for AI/ML integration, scripting, data workflows, and orchestration alongside the primary Java codebase |
NICE TO HAVE
- Experience navigating monorepo or large multi-repo architectures at enterprise scale — knowing how to orient quickly in a 1,700-repo codebase
- REST API design and versioning patterns in distributed microservices environments
- Hands-on experience with AI-assisted development workflows, prompt engineering for code generation, or LLM-powered developer tooling
- A consulting, product, or operations background alongside the engineering skills
- Enterprise delivery experience in regulated or complex environments (healthcare, finance, or similar)
THE HUMAN BAR
Technical depth is the entry point. Navigating complexity is the job.
When the system has 1,700+ repositories, the hardest part isn't writing code. It's knowing which code is worth writing, which system is safe to touch, and which problem is actually the one worth solving. The engineer who succeeds here reads architectures, earns trust from engineering teams quickly, and makes consequential build decisions without waiting for direction.
- Entrepreneurial self-starter — the most critical trait. Thrives in white space, presents options, tries things. Does not wait to be handed a scoped backlog.
- Communication — extracts real requirements by sitting with engineers and business stakeholders directly, not by reading documents. Validates ideas fast and keeps the right people informed without creating overhead.
- Architectural judgment — in a 1,700-service system, knowing where not to build is as important as knowing how to build. You read a codebase and form a point of view quickly.
- AI tooling fluency — doesn't just use Cursor and Copilot for autocomplete. Uses them to reason about architecture, generate tests, explore patterns in unfamiliar code, and move faster than the engineers around them.
- Ownership through adoption — the engagement is not done when the code is deployed. It is done when the client's team is running the solution independently.
WHAT MAKES THIS DIFFERENT
1,700+ repositories means this is one of the largest and most complex microservices environments you will work in. That complexity is the challenge — and also the opportunity. Small, well-placed interventions in a system this large can have disproportionate impact. Your job is to find those points and build there.
AI developer tooling is already in the stack — Cursor, Claude, and GitHub Copilot are expected tools, not a new pitch. You will be working alongside engineers who already use them. Your edge is using them more strategically: to orient faster in an unfamiliar codebase, to reason about architecture decisions, and to ship with more confidence.
The measure of success is explicit and non-negotiable: production deployment, active adoption, and a client team that can run the solution without you. That bar is higher than most engineering engagements — and it is what makes the work matter.
YOU'LL WORK INSIDE A PROPRIETARY AI DELIVERY FRAMEWORK
Most teams using AI tools today are flying blind — they feel faster but can't prove it. We built GenDD (Generative Driven Development) to change that, and you'll be running it from day one on this engagement.
GenDD is a proprietary methodology and toolset built around one belief: great engineers need full context to move fast without breaking things. In a 1,700-repository environment, that context is everything. GenDD gives you the scaffolding to orient quickly, run real sprints, and instrument every build decision so your work produces repeatable, client-ready evidence of impact.
Zero Ramp-Up | Proven Value | All-In Team |
Our tooling surfaces full project context — architecture, data flows, history — so you hit the ground running on every engagement. | GenDD instruments every agent interaction, commit, and deploy — turning your work into clear, client-ready evidence of real impact. |
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