Senior ML Search & LLM Ops (Contractor)

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 clientsincluding some of the worlds most successful hedge funds banks and asset managersto 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 years 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.

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.

• Education: Masters 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 testingcite: 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.

• 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.