RavenPack

Year Founded: 1999

Jobs at RavenPack

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Recently posted jobs

6 Days AgoSaved
Hybrid
New York, NY, USA
Lead full-cycle enterprise sales of AI, SaaS, API, and data solutions to financial institutions across the Americas. Manage strategic accounts, consultative sales cycles, pricing and negotiations, forecasting, executive relationships, and cross-functional collaboration with product, data science, and client success teams.
6 Days AgoSaved
Hybrid
Marbella, Málaga, Andalucía, ESP
Leads IT Support operations while remaining hands-on with escalations, systems administration, identity and endpoint management, automation, monitoring, security hygiene, and service governance. The role manages team capacity, mentoring, planning, reporting, vendors, budgets, change approvals, and delivery accountability. It requires coordinating reliable IT services across the business in a cloud-first environment.
13 Days AgoSaved
In-Office
Marbella, Málaga, Andalucía, ESP
Build and own AI automation projects from prototype to production-ready tools. Design agent workflows, select data sources and prompting strategies, evaluate accuracy, improve reliability, create reviewable outputs, and document solutions. The role requires Python or similar programming skills, API and data experience, hands-on LLM and agent development, AI-assisted productivity, attention to data quality, and clear English communication. Knowledge graphs, entity resolution, web research automation, scraping, RAG, and financial data experience are preferred.
One Month AgoSaved
In-Office or Remote
2 Locations
Develop and optimize search and LLM systems for financial applications. Responsibilities include fine-tuning open-source models, preference alignment, inference acceleration, quantization, retrieval optimization, modular AWS SageMaker and Docker pipeline delivery, and automated benchmarking of model quality, latency, and cost.
One Month AgoSaved
In-Office or Remote
2 Locations
Design, train, evaluate, and deploy advanced NLP and machine learning models within large-scale production systems. Build integration layers, continuous learning pipelines, evaluation frameworks, monitoring, and automated testing. Optimize models for low-latency inference, reliability, and scale while integrating probabilistic models with deterministic or legacy architectures.