Senior ML / Search & LLM Ops (Contractor)

Location: Marbella or Madrid; fully remote. European legal working status and EU timezone alignment are required.

Compensation: Competitive daily or project-based contract rate, commensurate with experience.

Company

hirify.global develops generative AI and SaaS services for financial professionals, building on 23 years of big data analytics experience for financial services.

What you will do

• Implement and evaluate domain-specific open-source LLM adaptations using PEFT, LoRA, QLoRA, and distillation for financial applications.
• Prototype preference alignment mechanisms such as DPO and PPO for specialized tasks.
• Benchmark and optimize model serving for low latency and high throughput using quantization and inference engines including Triton, TEI, and vLLM.
• Develop advanced search and retrieval pipelines using Matryoshka embeddings, late interaction models, and hybrid structured/unstructured search.
• Package training, evaluation, and serving scripts into reproducible deliverables with AWS SageMaker and Docker.
• Build synthetic data generation and automated evaluation harnesses to measure cost, latency, and quality trade-offs.

Requirements

• Master’s or PhD in Computer Science, Machine Learning, or a quantitative field, or equivalent practical experience.
• Production experience delivering ML models and PoCs in Search, Information Retrieval, or LLM infrastructure.
• Deep hands-on experience with Python, PyTorch, and the Hugging Face ecosystem, including Transformers, PEFT, and Accelerate.
• Practical experience with model quantization, VRAM optimization, and high-performance serving frameworks.
• Experience with AWS SageMaker, experiment tracking, and tracing tools such as MLflow and Opik.
• Fluent English communication skills and European legal working status with EU timezone alignment.

Nice to have

• Experience implementing RLHF or Direct Preference Optimization.
• Familiarity with financial market data and financial text processing.

Culture & Benefits

• Initial 3–6-month contractor engagement with potential extension based on project milestones and results.
• Independent contributor role embedded with the internal Search & Recommendation engineering team.
• Autonomous execution with an emphasis on well-documented, modular code and empirical validation.
• Daily or project-based contract compensation commensurate with experience.