Senior ML / Search & LLM Ops (Contractor)

About the Position

Senior ML / Search & LLM Ops (Contractor)

RavenPack is at the forefront of developing the next generation of generative AI tools for the finance industry. With 23 years of experience as a leading big data analytics provider for financial services, we empower our clients to enhance returns, reduce risk, and increase efficiency by integrating public information into their models and workflows. We are launching a new suite of GenAI and SaaS services, designed specifically for financial professionals.

European legal working status is required.

Responsibilities

• 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.

Requirements

• 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.
• Language & Status: Fluent English communication skills (written and verbal). European legal working status / EU timezone alignment required.

Nice to Have

• 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 Offer

• Competitive Compensation: Daily or project-based contract rate commensurate with experience.
• Flexible Work Model: Fully remote position with the option to work from Marbella or Madrid.
• Professional Growth: Opportunity to work on cutting-edge AI projects in the finance industry.

About the Company

RavenPack is a global leader in financial data and intelligence, helping organizations turn vast amounts of unstructured information into decision-ready insight. Since 2003, we have focused on solving one of finance’s most persistent challenges: data is abundant, but usable data is scarce. By removing data friction across the entire lifecycle - from sourcing and preparation to deployment and action - RavenPack enables faster, clearer, and more confident decisions in high-stakes environments.

Trusted by leading global financial institutions and used by approximately 70% of the world’s top-performing hedge funds, RavenPack combines deep financial domain expertise with proven data engineering and AI capabilities at scale. With more than 200 employees across the U.S. and Europe, the company has established itself as a gold standard for trusted, auditable, and production-ready financial intelligence.

RavenPack offers two complementary products: RavenPack Edge, designed for quantitative and systematic investment workflows, and Bigdata.com, an AI-native platform for financial research and agentic systems built on trusted data.

Bigdata.com enables professionals to deploy ready-made AI agents for instant insights or build their own via APIs-speeding up research, automating workflows, and driving better investment decisions.

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.