Software Engineer

Job Summary

The Software Engineer (Human Data Platforms) architectures, secures, and scales full-stack infrastructure supporting AI evaluation datasets. Core daily duties include developing robust backend microservices using Python and FastAPI, building rich data orchestration interfaces using React and Next.js, modeling complex database structures in PostgreSQL, building secure cloud data pipelines across AWS storage nodes, implementing Optical Character Recognition (OCR) and vector search indexing for scanned payloads, and mapping nested JSON structures with strict schema validation rule sets.

Foundational Capabilities & Prerequisites

• Full-Stack Technical Versatility: Demonstrated ability to build, optimize, and ship systems comfortably across the entire stack—spanning APIs, interactive user interfaces, and asynchronous data pipelines.
• Python Core Proficiency: Advanced, hands-on production engineering experience using Python, specifically utilizing FastAPI or highly similar asynchronous framework variations.
• Frontend Ecosystem Depth: Strong experience building responsive, stateful web frontends using React, Next.js, and TypeScript.
• Database & Architecture Fundamentals: Solid, uncompromised fundamentals across REST/GraphQL API design, SQL database modeling (specifically PostgreSQL), and asynchronous event-driven systems.
• Cloud Infrastructure Literacy: Practical experience working within major public cloud environments, primarily AWS (or equivalent GCP/Azure implementations), with a focus on storage architectures, identity controls, and scaling parameters.
• Data System Management: Proven ability to manipulate, map, and process complex data systems, nested array structures, or streaming data pipelines.
• Execution & Autonomy Mindset: A strong builder persona characterized by high personal ownership, a low ego, fast execution habits, and the capacity to guide ambiguous features from ideation to production deployment.

Preferred Technical Multipliers

• Frontier AI Ecosystem Exposure: Direct professional experience working with Large Language Models (LLMs), prompt tokenization spaces, or AI evaluation control layers.
• Document Sifting Core Experience: Prior experience architecting OCR (Optical Character Recognition) frameworks, vector search architectures, or complex PDF generation/annotation systems.
• Data Governance Depth: Experience managing strict data privacy, enterprise-grade access control permissions, and verified zero-data-leakage storage isolation layers.
• Shipped System Proof: A history of building and scaling real-world, high-concurrency systems currently servicing production traffic (as opposed to standalone bootcamps or theoretical tutorials).

Technical Stack Architecture

As a Core Platform Engineer, you will actively guide and expand our primary technological stack:

• Backend Foundation: Python, FastAPI, Asynchronous Event Workers, Pydantic Schema Layers
• Frontend Foundation: React, Next.js, TypeScript, Stateful UI Managers, TailwindCSS Component Layers
• Storage & Database Engine: PostgreSQL, Advanced SQL Indexing, Relational Schema Management
• Cloud & Security Infrastructure: AWS (S3, IAM, CloudWatch, EC2), secure cross-cloud storage abstractions (GCS / Azure Blobs)
• Document & Data Utilities: OCR Engines, Vector Search Indices, Complex Nested JSON Parsers, Automated PDF Generator Tooling

Key Responsibilities

1. Full-Stack Feature Delivery & API Engineering

• Architect, implement, and ship clean frontend interfaces and high-performance backend microservices using Python (FastAPI) and React (Next.js/TypeScript).
• Build resilient, self-documenting web APIs optimized for low latency, secure data transport, and high front-end client rendering performance.
• Maintain absolute code clarity, ensuring type safety across the stack by matching TypeScript models on the frontend with Pydantic schemas on the backend.

2. Cloud Pipeline Engineering & Data Integrity

• Build and maintain scalable data pipelines that serve as the foundation for AI evaluation layers and automated model control systems.
• Refine data processing workflows, parsing, enforcing, and restructuring deeply nested, highly variable JSON data payloads.
• Debug, optimize, and scale data storage engines and query pipelines, proactively eliminating structural performance bottlenecks as traffic patterns expand.

3. Document Processing, OCR & Search Infrastructure

• Develop and optimize advanced data parsing systems equipped to process text extraction, OCR layers, and semantic search queries across raw PDFs, images, and scanned physical forms.
• Build advanced document rendering utilities that empower our global expert workforce to generate, edit, and annotate complex PDF and graphic assets directly inside their browser workspace.
• Implement structured database index paradigms to make multi-page extracted documents instantly searchable.

4. Enterprise Security Controls & Data Leakage Prevention

• Own the architectural security of our core document pipelines across AWS S3, Google Cloud Storage, or Azure Blob environments.
• Design and enforce strict user permission matrices and role-based access controls (RBAC) to ensure unauthorized tenants never access private datasets.
• Implement strict data validation guardrails to ensure total isolation and prevent data leakage between discrete fine-tuning pipelines.

Core Competencies & Engineering Strengths

• Asynchronous System Optimization: Designing and debugging non-blocking event loops, queue workers, and background threads to process large document batches without halting user-facing services.
• Relational Schema Precision: Writing highly optimized SQL queries, indexing strategies, and normalization models in PostgreSQL to ensure rapid data access under heavy reads.
• Defensive Architecture Logic: Building secure file handling systems that strictly validate content types, MIME flags, and access permissions, keeping user data safe from injection or exfiltration.
• UI Component Modularization: Crafting reusable, well-typed React/TypeScript components that balance performance with clean, accessible design.
• Deterministic Problem Solving: Approaching system bugs systematically by relying on structured log analysis, tracing metrics, and telemetry data rather than guesswork.

Skills Required:

• Computer / Software / It / Data