[Hiring] Senior ML / Search & LLM Ops @RavenPack

Role Description

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.

Qualifications

• 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.

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.

Company Description

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.

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.