Senior GenAI & Search Engineer (LLM Ops)

RavenPack; España; On-site; EUR 101,000 - 157,000; Full time

Job summary

RavenPack, a leader in AI for finance, is seeking an autonomous ML engineer to advance our GenAI/LLM stack. You will prototype, optimize, and deploy domain‑specific models and PoCs, focusing on fast, scalable inference and retrieval systems.

Builds on a strong foundation in Python, PyTorch, and Hugging Face, with experience in PEFT, quantization, and AWS SageMaker. This role demands independent work and a results‑driven mindset.

Qualifications

• Master’s or PhD in Computer Science, Machine Learning, or a quantitative field (or equivalent practical experience).
• Proven track record delivering production‑grade ML models and PoCs in Search, Information Retrieval, or LLM infrastructure.
• Deep hands‑on experience with Python, PyTorch, and the Hugging Face ecosystem (Transformers, PEFT, Accelerate).
• Inference & optimization: practical experience with model quantization, VRAM optimization, and high‑performance serving frameworks (Triton, TEI, vLLM).
• MLOps & Deployment: experience using AWS SageMaker for training/deployment, with experiment tracking and tracing tools (MLflow, Opik).
• Execution style: autonomous, modular code, rapid empirical validation.

Responsibilities

• Domain‑Specific Fine‑tuning & PoCs for financial contexts using PEFT and Distillation.
• High‑Performance Inference Acceleration: optimize model serving for low latency and high throughput.
• Search & Retrieval Optimization: prototype advanced search techniques and hybrid pipelines.
• Modular Pipeline Delivery: package training, evaluation, and serving scripts into reproducible deliverables.
• Evaluation & Efficiency Benchmarking: measure cost, latency, and quality trade‑offs for PoCs.

Skills

Fluent English

Autonomous work style

Empirical testing

Education

Master's or PhD in CS/ML

Tools

Python

PyTorch

Transformers (Hugging Face)

PEFT

Accelerate

SageMaker

Docker

Job description

RavenPack, a leader in AI for finance, is seeking an autonomous ML engineer to advance our GenAI/LLM stack. You will prototype, optimize, and deploy domain‑specific models and PoCs, focusing on fast, scalable inference and retrieval systems.

Builds on a strong foundation in Python, PyTorch, and Hugging Face, with experience in PEFT, quantization, and AWS SageMaker. This role demands independent work and a results‑driven mindset.