Senior ML / Search & LLM Ops (Contractor)

RavenPack; Madrid; Presencial; EUR 65.000 - 138.000; Jornada completa

Descripción de la vacante

RavenPack is hiring for an independent contributor to advance GenAI and SaaS offerings for finance. You will work on domain-specific fine-tuning, high-performance inference, and advanced search/retrieval optimizations within an LLM stack.

The role requires a Master’s/PhD or equivalent practical experience, strong English communication, and EU working status. Contract is 3–6 months with potential extension, based on milestones.

Formación

• Education: Master’s or PhD in Computer Science, Machine Learning, or a quantitative field.
• Proven track record of delivering production-grade ML models and PoCs in Search, Information Retrieval (IR), or LLM infrastructure.
• Core Tech Stack: 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: AWS SageMaker for training/deployment, MLflow, Opik.

Responsabilidades

• Direct Hands-on Execution, Rapid Prototyping, And Delivering Plug-and-play Optimizations For Our Search And LLM Stack.
• Prototype and evaluate domain-specific fine-tuning & PoCs using PEFT (LoRA, QLoRA) for financial contexts.
• Benchmark and optimize model serving for low latency and high throughput; apply quantization and specialized engines.
• Prototype and validate advanced search techniques and hybrid search pipelines.
• Package training, evaluation, and serving scripts into clean, reproducible deliverables using AWS SageMaker and Docker.
• Set up synthetic data generation and automated evaluation harnesses to measure trade-offs for PoCs.

Conocimientos

Python

PyTorch

Hugging Face

Transformers

PEFT

Accelerate

AWS SageMaker

Docker

mlops

English

Educación

Master’s or PhD in Computer Science, ML or quantitative field

Herramientas

Triton Inference Server

TEI

vLLM

LoRA

QLoRA

Distillation

Opik

Descripción del empleo

About Us
At RavenPack, we are at the forefront of developing the next generation of generative AI tools for the finance industry and beyond. With 23 years of experience as a leading big data analytics provider for financial services, we empower our clients—including some of the world's most successful hedge funds, banks, and asset managers—to enhance returns, reduce risk, and increase efficiency by integrating public information into their models and workflows. Building on this expertise, we are launching a new suite of GenAI and SaaS services, designed specifically for financial professionals.

About Us
At RavenPack, we are at the forefront of developing the next generation of generative AI tools for the finance industry and beyond. With 23 years of experience as a leading big data analytics provider for financial services, we empower our clients—including some of the world's most successful hedge funds, banks, and asset managers—to enhance returns, reduce risk, and increase efficiency by integrating public information into their models and workflows. Building on this expertise, we are launching a new suite of GenAI and SaaS services, designed specifically for financial professionals.

Join a Company that is Powering the Future of Finance with AI

RavenPack has been recognized as the Best Alternative Data Provider by WatersTechnology and has been included in this year’s Top 100 Next Unicorns by Viva Technology. RavenPack has launched Bigdata, our Gen-AI platform tailored for finance, which is already being recognized as the #1 platform for powering financial AI agents.

European Legal Working Status Is Required.

Project Scope & Core Responsibilities

Direct Hands-on Execution, Rapid Prototyping, And Delivering Plug-and-play Optimizations For Our Search And LLM Stack. Examples Of What You Could Be Working On Include

• Domain-Specific Fine-tuning & PoCs: Implement and evaluate targeted open-source LLM adaptations using PEFT (LoRA, QLoRA) and Distillation tailored to financial contexts. Prototype preference alignment mechanisms (DPO/PPO) for specialized tasks.
• High-Performance Inference Acceleration: Benchmark and optimize model serving for low latency and high throughput. Apply quantization techniques (AWQ, GPTQ) and leverage specialized engines (Triton Inference Server, TEI, vLLM).
• Search & Retrieval Optimization: Prototype and validate advanced search techniques, including Matryoshka embeddings, late interaction models, and hybrid search pipelines combining structured and unstructured data.
• Modular Pipeline Delivery: Package training, evaluation, and serving scripts into clean, reproducible deliverables using AWS SageMaker and Docker.
• Evaluation & Efficiency Benchmarking: Set up synthetic data generation and automated evaluation harnesses (e.g., Opik, LLM-as-a-judge) to measure cost, latency, and quality trade-offs for delivered PoCs.
  

What We’re Looking For

• Education: Master’s or PhD in Computer Science, Machine Learning, or a quantitative field (or equivalent practical experience).
• Professional Experience: Proven track record of delivering production-grade ML models and PoCs in Search, Information Retrieval (IR), or LLM infrastructure.
• Core Tech Stack: 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, combined with experiment tracking and tracing tools (MLflow, Opik).
• Execution Style: Ability to work autonomously, deliver well-documented, modular code, and rapidly validate ideas through empirical testing[cite: 2, 3].
• Language & Status: Fluent English communication skills (written and verbal). European legal working status / EU timezone alignment required.

Bonus Points:

• Direct experience implementing RLHF or Direct Preference Optimization (DPO).
• Familiarity with financial market data and financial text domain processing.
  

Engagement Terms

• Contract Duration: Initial 3 to 6-month contract engagement with potential for extension based on project milestones and results.
• Working Model: Independent contributor embedded with the internal Search & Recommendation engineering team.
• Compensation: Competitive daily or project-based contract rate commensurate with experience.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.