Ai Software Engineer — Python, Document Intelligence & Aws (Remote)
NextGen Coding Company · Manhattan, United States ·
- Employment
- Contract
- Category
- AI engineering
- Visa sponsorship
- No
- Salary
- USD 30 / hour
NextGen Coding Company · Manhattan, United States ·
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Contract AI Software Engineer role (hybrid/in-person in NYC) building an enterprise document-intelligence platform on AWS: ingesting PDFs, scans, and HTML, running OCR and Python-based processing, and making the structured data searchable and usable by LLMs via RAG, embeddings, and hybrid search.
AI Software Engineer — Python, Document Intelligence & AWS
NextGen Coding Company
Location: New York City — Hybrid / In-Person Required
Compensation: $30/hour
Hours: 40 Hours/Week
Engagement: Contract with potential for long-term engagement
Eligibility: Must be a U.S. citizen and able to complete a W-9. No visa sponsorship is available for the role.
Role Overview
NextGen Coding Company is hiring an AI Software Engineer in New York City to join an active enterprise AI project focused on document intelligence, large-scale data processing, and evidence-backed AI analysis.
The work is hands-on and engineering-heavy. You will help build a cloud platform that ingests thousands of pages of PDFs, scans, images, HTML, and other data;
performs OCR and Python-based processing;
structures and indexes the resulting information;
and makes the data searchable and usable by modern LLMs.
We are looking for a strong builder, not someone whose AI experience consists primarily of calling an LLM API.
You should be comfortable jumping directly into an existing project, understanding the architecture, debugging difficult data-processing problems, and shipping production code.
What You’ll Work On
Core Technical Skills
Python: FastAPI, data processing, ETL, APIs, asynchronous/background jobs
Document Intelligence: OCR, PDF parsing, scanned documents, OpenCV, PyMuPDF, PaddleOCR, Tesseract, Docling, Unstructured, or similar tools
AI: Claude, OpenAI, Gemini, Qwen, RAG, embeddings, structured outputs, reranking, model orchestration
Data: PostgreSQL, SQL, OpenSearch/Elasticsearch, vector databases, Redis
AWS: S3, RDS, OpenSearch, Bedrock, EC2/ECS/EKS, Lambda, SQS, IAM, CloudWatch
Infrastructure: Docker, Linux, CI/CD, production cloud deployments
Frontend experience with React, Next.Js, and TypeScript is helpful but is not the primary focus.
Who We Want
We want someone who can be given a difficult engineering problem and figure it out.
For example:
“We have several thousand pages across PDFs, scans, images, and other file formats. Large files are processing inconsistently. Build a reliable AWS pipeline that classifies the files, performs OCR where required, cleans and structures the output with Python, handles failures, indexes the information, and makes the evidence usable by an LLM.”
You should be able to break a problem like the one above into an architecture and then actually build it.
Strong candidates will have experience building real production systems involving Python, data pipelines, OCR/document processing, AWS, or AI.
Requirements
Interview Process
We are intentionally keeping the process straightforward:
1. Application Review — Resume, LinkedIn, GitHub, and/or examples of systems you have built.
2. Technical Screen — Python, OCR/document processing, AWS, data architecture, and LLM engineering.
3. In-Person NYC Meeting — Meet the team, walk through the project, and discuss how you would approach real engineering problems from the platform.
We are looking for someone who can join quickly, take ownership, and immediately contribute to a technically ambitious production AI system.
About NextGen Coding Company
NextGen Coding Company is a U.S.-based software engineering firm building custom software, AI systems, automation platforms, data infrastructure, and enterprise applications.
Our engineers work on real production systems across AI/ML, document intelligence, data engineering, financial and compliance technology, and cloud infrastructure.
To Apply
Please send:
NYC candidates only.
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