About the role
We're looking for a mid-level full stack engineer to build features end to end across a Python/FastAPI backend and a React/TypeScript frontend. A large share of the work involves LLM-powered product features: an AI agent that calls tools to read and write real project data, and pipelines that pull structured data out of messy documents.
Location:LATAM 100% Remote. Working hours are based on the US Central Time Zone. Minimum 6-hour overlap
About the Company:
Abstra is a fast-growing, Nearshore Tech Talent services company, providing top Latin American tech talent to U.S. companies and beyond. Founded by U.S.-bred engineers with over 15 years of experience, Abstra specializes in sourcing skilled professionals across a wide range of technologies to meet our clients’ needs, driving innovation and efficiency.
Key Responsibilities
• Build and ship full stack features
• Create fast proof of concept work for demos and client feedback
• Extend the AI agent: add and maintain tools with typed input and output schemas, refine system
prompts, and keep model outputs grounded in data from the database.
• Measure LLM behavior with evaluation suites and use the results to guide changes to prompts,
tools and models. Changes should be backed by evals, not hunches.
• Improve document extraction and classification pipelines for PDFs, Excel, Word, and CSV files,
including the LLM-assisted steps.
• Build data-heavy UI: tables, charts, maps and streaming chat interfaces.
• Write tests at every layer (pytest, Vitest, Playwright) and review teammates' pull requests.
• Contribute to infrastructure as code and CI/CD when your features need it.
Required experience
• 3–5+ years of professional software engineering, with full stack features shipped to production.
• Strong object-oriented design. You can model a complex domain with clear classes, sensible
boundaries and composition, and you know when inheritance is the wrong tool. Comfortable
with SOLID principles, common design patterns, and refactoring legacy code toward a cleaner
structure.
• LLMs and ML as product features. You've built and shipped at least one feature where an LLM or
ML model is part of what the product does for customers. Using AI coding assistants doesn't
count toward this. Specifically:
o Tool or function calling, and structured outputs validated against schemas
o Prompt design, versioning, and checking outputs against source data to limit
hallucinations
o Evaluating model behavior with test sets or eval suites, and handling cost, latency and
failure modes
o Streaming LLM responses to a user interface
• Python backend: production experience with FastAPI or a similar framework, Pydantic, async
Python, and SQLAlchemy (or a comparable ORM) with migrations.
• Relational databases: strong PostgreSQL skills, including schema design, query performance and
data integrity.
• React and TypeScript: production experience with modern hooks-based React, server-state
management (TanStack Query or similar), and building complex, data-dense interfaces.
• Testing: you write automated tests by habit, both unit and integration, on the backend and the
frontend.
• Cloud and delivery: working experience with AWS, Docker, Git-based pull request workflows and
CI/CD pipelines.
• Communication: professional working English, and you can work independently in a small
remote team. That means clear written updates, good pull request descriptions, and raising
blockers early.
• Hands-on experience with pydantic-ai, the Vercel AI SDK, or comparable agent and LLM
frameworks such as LangChain, LangGraph or the OpenAI Agents SDK.
• Retrieval and RAG systems: embeddings, vector search (pgvector or similar), and hybrid retrieval.
• Document AI: PDF and table extraction, OCR, and classifying semi-structured data.
• Classical ML or applied statistics: regression, estimation, and working with confidence ranges.
• Terraform and AWS App Runner or ECS.
• Geospatial work: Shapely, PostGIS, or Leaflet-style mapping.
• Domain exposure to real estate, construction, finance or proptech, such as cost estimating or pro
forma modeling.
• Security-minded engineering: multi-tenant data isolation, authorization models and secrets
handling.
What We Offer:
Competitive compensation paid in USD.
20 days of paid time off (PTO) per year.
Opportunities for professional growth and career development.
Company-provided equipment.
A collaborative, inclusive, and multicultural work environment.
The opportunity to contribute to meaningful projects alongside a talented and supportive team.
Pre-Employment Verification
As part of our standard onboarding process, candidates who successfully complete the interview process and accept an employment offer will be required to complete an employment verification check, and background check. This process will confirm job titles and dates of employment with two previous employers and is a standard requirement for all new employees joining the company.
Originally posted on Himalayas