Senior ML / Search & LLM Ops (Contractor)

RavenPack

About The Position

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.

Requirements

Master’s or PhD in Computer Science, Machine Learning, or a quantitative field (or equivalent practical experience).
Proven track record of delivering production-grade ML models and PoCs in Search, Information Retrieval (IR), or LLM infrastructure.
Deep hands-on experience with Python, PyTorch, and the Hugging Face ecosystem (Transformers, PEFT, Accelerate).
Practical experience with model quantization, VRAM optimization, and high-performance serving frameworks (Triton, TEI, vLLM).
Experience using AWS SageMaker for training/deployment, combined with experiment tracking and tracing tools (MLflow, Opik).
Ability to work autonomously, deliver well-documented, modular code, and rapidly validate ideas through empirical testing.
Fluent English communication skills (written and verbal).
European legal working status / EU timezone alignment required.

Nice To Haves

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

Responsibilities

Direct hands-on execution, rapid prototyping, and delivering plug-and-play optimizations for our search and LLM stack.
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.
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).
Prototype and validate advanced search techniques, including Matryoshka embeddings, late interaction models, and hybrid search pipelines combining structured and unstructured data.
Package training, evaluation, and serving scripts into clean, reproducible deliverables using AWS SageMaker and Docker.
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.