AI/ML Engineer

AI/ML Engineer – LLM & Data Platform

Position Overview

We're looking for an AI/ML Engineer to own our end-to-end LLM and data platform at Pruvo. This is a hybrid role that blends data engineering rigor with applied machine learning — you'll design, build, evaluate, and deploy intelligent systems that sit at the core of our product.

About Pruvo

Pruvo is a fast-growing travel-tech company focused on helping travel companies increase profitability and generate additional sales by improving sales channels, dynamic pricing models, and many more.

At Pruvo, we combine data intelligence, automation, and scalable backend architecture to power solutions for large enterprise clients in the travel space. Our platform is designed to integrate seamlessly into partner ecosystems, enabling organizations to deliver continuous value to their customers through automated price optimization. As we expand our B2B offering and infrastructure, we're looking for experienced engineers who are excited to build robust, high-scale systems that serve complex business needs.

The Opportunity

This is a greenfield, high-ownership role. You'll be the go-to person for everything LLM and data at Pruvo — from evaluating which models and frameworks best fit our use cases, to fine-tuning and deploying them on our AWS infrastructure. You'll work closely with the backend and product teams to integrate AI capabilities into our core platform, and you'll help establish the engineering practices that will define how we build with AI going forward.

What Makes You a Great Fit

• You're comfortable living at the intersection of data engineering and machine learning — you don't just train models, you build the pipelines that feed them and the infrastructure that runs them.
• You have hands-on experience with LLMs — whether through fine-tuning, RAG architectures, prompt engineering, or evaluation frameworks.
• You care deeply about measurable outcomes: you build eval suites before you ship, and you iterate based on data.
• You're self-directed and thrive in an environment where you define the problem as much as you solve it.

What You'll Be Doing

• Own the full LLM lifecycle — from problem framing and model selection through evaluation, deployment, and monitoring in production.
• Evaluate LLM solutions within Pruvo's systems — design and run rigorous benchmarks to compare models (commercial and open-source) against our specific use cases.
• Build and maintain the data platform — design reliable, scalable pipelines for data ingestion, processing, and serving that feed our ML systems.
• Fine-tune and retrain models — work with open-source models (e.g. Llama, Mistral, Qwen), adapt them to our domain, and deploy them on AWS (self-hosted inference).
• Design AI-powered features — collaborate with product and engineering to translate business needs into well-scoped AI solutions.
• Establish best practices — define how we evaluate, version, and monitor models; bring structure to a fast-moving space.

A Typical Day

• Reviewing evaluation results from a new fine-tuned model variant and deciding whether it's ready to replace the current production model.
• Working with a backend engineer to design the data schema and ingestion pipeline for a new signal we want to incorporate.
• Running a prompt engineering experiment, logging results to our eval framework, and writing up findings.
• Coordinating with DevOps on the deployment of a self-hosted model endpoint.
• Joining a product sync to scope an upcoming AI feature and identify the data requirements.

Tech Stack

• Languages: Python (primary), TypeScript
• LLM frameworks: LangChain / LlamaIndex, Hugging Face Transformers, vLLM or similar inference servers
• Open-source models: Llama, Mistral, Qwen, or equivalent
• Data: SQL, data pipeline tooling (Airflow, dbt, or similar), vector databases
• MLOps: experiment tracking (MLflow / W&B), model registries, CI/CD for ML
• Backend context: LoopBack-based API, GitLab + GitHub

Requirements

• 8+ years of hands-on experience in a data engineering, ML engineering, or applied AI role.
• Solid experience with LLMs — at minimum fine-tuning or building RAG/agentic systems in production.
• Strong Python skills; experience building and maintaining data pipelines.
• Familiarity with open-source model ecosystems (Hugging Face, model quantization, inference optimization).
• Experience deploying ML models to cloud infrastructure (AWS preferred).
• Strong understanding of ML evaluation methodology — you know how to measure what matters.
• Comfortable working in a small, fast-moving team with high autonomy.

Nice to Have

• Experience with self-hosted LLM inference (vLLM, TGI, Ollama).
• Familiarity with travel-tech, pricing models, or B2B SaaS data patterns.
• Experience with TypeScript / Node.js backend environments.
• Contributions to open-source ML projects.
• Knowledge of MLOps tooling (MLflow, W&B, DVC).

Compensation & Benefits

• Competitive salary based on experience.
• Remote-friendly with flexibility.
• Direct ownership and impact — this is not a support role.
• Work with a talented, tight-knit engineering team on real-world AI problems at scale.

Hiring Process

• 15 minutes interview using AI tool.
• Introductory conversation
• Practical take-home task
• Technical discussion based on your approach
• Final interview with engineering leadership
• Offer stage

If you’re excited about turning data into real-world impact and building intelligent systems that power decision-making for global businesses, Pruvo could be a great fit.