Python AI Engineer — LLM/NLP/A

Our B2C SaaS products transform complex tasks — from creating standout resumes to generating legal documents — into simple, engaging user experiences.

pdf.net is the fastest-growing product in our portfolio. Thanks to bold product breakthroughs, it already serves hundreds of thousands of users every month, and is on track for even greater growth.

We’re now looking for a Python AI Engineer with deep expertise in Natural Language Processing (NLP) and Large Language Models (LLMs). This is a hybrid role that blends custom model development with LLM API integration to ship intelligent, production-ready features. You’ll work across the full lifecycle—from preparing training data and fine-tuning models, to designing retrieval pipelines and deploying performant inference systems in the cloud.

Variant Products

• resume.co Live since May 2023, ±1M MAU

• pdf.net Live since Dec 2024, ±400k MAU

• contracts.net Live since January 2023, ±100k MAU

• mealplan.co – Summer 2025

What You’ll Do

• Integrate hosted LLM APIs (e.g. OpenAI, Anthropic) + custom models to support intelligent in-product behavior.

• Build and fine-tune transformer models using PyTorch, HuggingFace

• Design and deploy retrieval-augmented generation (RAG) pipelines with vector databases (e.g., pgvector) and graph-based reasoning (e.g., Neo4j).

• Develop scalable inference systems using vLLM, speculative decoding, and optimized serving techniques.

• Build modular, production-grade pipelines for training, evaluation, and deployment.

• Collaborate closely with product, design, and full-stack teams to ship features that bring AI to end users.

• Own infrastructure around Docker, Cloud Run, and GCP, ensuring speed, reliability, and observability.

What You Bring

• Strong Python engineering background with clean, tested, and maintainable code.

• Proven experience building with transformer-based models, including custom training and fine-tuning.

• Deep familiarity with HuggingFace, PyTorch, tokenization, and evaluation frameworks.

• Experience integrating and orchestrating LLM APIs (OpenAI, Anthropic) into user-facing products.

• Understanding of semantic search, vector storage (FAISS, pgvector), and hybrid symbolic-neural approaches.

• Experience designing or consuming graph-based knowledge systems (e.g., Neo4j, property graphs).

• Ability to build and debug scalable training and inference systems.

Bonus Points For

• Hands-on experience with Docker and production deployment on Google Cloud (GKE, Cloud Run).

• Experience with RLHF, reward models, or reinforcement learning for LLM alignment.

• Knowledge of document understanding, OCR, or structured PDF parsing.

• Exposure to monitoring and observability tools (e.g., Prometheus, Grafana, OpenTelemetry).

• Background in linguistics, semantics, or computational reasoning.

Working with us

• Pure Data-Driven SaaS. Learn and experiment, no opinion-driven decisions.

• Location Independence. Work from a location that suits you - be it your home, a café, or anywhere else.

• Unlimited Time Off. We trust you to take time off when you need, ensuring you’re always at your best when you’re with us.

• Results-Oriented. We believe in the quality of output over hours clocked in. Deliver excellence, and enjoy unparalleled autonomy in return.

Salary & Contract

• Compensation: $60,000 — $90,000/year gross. Yearly bonus based on profit and performance.

• For the right person, we’re open to crafting a package that goes well beyond — including equity and fast-track opportunities.

• Paid via deel.com (standard contract).

• Asia/Pacific or EU timezone.

How to apply?

1. Submit via Google Forms (5-8 mins).

2. We'll invite you for a call with our Lead AI Engineer.

3. If we see a potential match, you'll be invited to undertake a small challenge, requiring up to 8 hours of your time.