Machine Learning Engineer, Crypto Platform - Geneva

Machine Learning Engineer AP Executive are working with an institutional-grade crypto intelligence terminal. They aim to provide comprehensive news coverage, research, and data products to industry participants. We are seeking a machine learning engineer with deep expertise in building production AI systems that handle complex, real-time data at scale.

The Role You will architect and build an AI system for cryptocurrency market intelligence – a sophisticated application that synthesizes multiple data sources including market data, news, and technical analysis to answer complex market questions that professional traders ask.

You will play a key role in defining the technical approach for this project – helping evaluate whether to leverage existing foundation models with fine-tuning, build custom architectures, or use API-based solutions. This is a greenfield opportunity where you\’ll contribute significantly to architectural decisions based on accuracy requirements, cost constraints, latency targets, and scalability needs.

Key responsibilities include :

Contributing to defining the overall technical approach and architecture – helping evaluate whether to use existing foundation models (GPT-4, Claude, Gemini) with RAG, fine-tune open-source models, or build custom solutions from scratch

Designing and implementing a RAG system that retrieves and synthesizes information from multiple heterogeneous data sources in real-time

Building strict hallucination prevention mechanisms – the system must only make claims backed by source data and explicitly state uncertainty when appropriate

Implementing sophisticated time-awareness and data freshness tracking across all queries and responses

Creating systems that synthesize multi-dimensional market signals into coherent analysis

Integrating outputs from quantitative systems and presenting them effectively through conversational interfaces

Building scenario-based reasoning capabilities that provide probabilistic assessments rather than deterministic predictions

Collaborating with data scientists and quantitative researchers to define optimal data structures and integration patterns

Creating conversational flows that intelligently gather context, ask clarifying questions, and guide users to relevant insights

Developing prompt engineering frameworks and guardrails that maintain consistent, reliable behavior

Building comprehensive monitoring systems to detect degraded performance, factual errors, or hallucinations in production

Implementing source citation and data provenance tracking throughout the system

Designing APIs and interfaces between the AI system and other infrastructure components

Autonomy and Approach You will have significant input in defining the technical approach to this project. You\’ll help evaluate whether to use existing foundation models via APIs, fine-tune open-source models, or build custom architectures based on accuracy requirements, cost constraints, latency targets, and scalability needs.

Beyond architecture, you\’ll contribute to choosing RAG frameworks, retrieval strategies, and implementation patterns. However, certain principles are non-negotiable: factual accuracy above all else, explicit uncertainty handling, source citations for claims, and scenario-based reasoning rather than overconfident predictions. These aren\’t optional features – they\’re fundamental requirements for building systems that users trust with high-stakes decisions.

You will help make critical decisions about when to use retrieval versus when models can reason directly, how to structure prompts for consistent behavior, and how to balance response quality with latency.

Candidate Profile Qualifications Experience and Track Record :

Minimum 5+ years of experience building and deploying machine learning systems in production

Proven experience contributing to architectural decisions for AI / ML projects – helping evaluate API-based solutions, fine-tuning approaches, and custom implementations based on product requirements

Demonstrated track record of evaluating and selecting appropriate LLM solutions (foundation model APIs, open-source models, custom architectures) for production use cases

Proven experience with large language models, RAG architectures, or conversational AI systems in production

Track record of building AI products that prioritized factual accuracy and appropriate handling of uncertainty

Experience working with real-time data integration and multi-source information synthesis

Portfolio of implemented LLM systems or contributions demonstrating technical depth in modern AI architectures

LLM and RAG Expertise :

Strong proficiency with modern LLM frameworks (LangChain, LlamaIndex, or custom implementations)

Deep understanding of RAG architectures including retrieval strategies, re-ranking, and context window management

Experience with vector databases and semantic search (Pinecone, Weaviate, Chroma, or similar)

Practical knowledge of prompt engineering, few-shot learning, and techniques for consistent LLM behavior

Understanding of hallucination detection and mitigation strategies in production systems

Experience with LLM fine-tuning, RLHF, or model adaptation techniques (highly valued)

Knowledge of LLM inference optimization and deployment (vLLM, TensorRT-LLM, or similar)

Technical Capabilities :

Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, or JAX)

Experience building conversational flows and multi-turn dialogue systems

Solid understanding of natural language processing fundamentals

Proficiency with API design for AI systems including streaming responses and session management

Experience with real-time data pipelines and event-driven architectures

Knowledge of caching strategies and performance optimization for LLM systems

Understanding of evaluation frameworks for generative AI (factual accuracy, relevance, hallucination detection)

Familiarity with MLOps practices specific to LLM deployments

Data and Domain Knowledge :

Experience working with time-series data and understanding temporal context in queries

Familiarity with financial markets, trading concepts, or similar high-stakes decision-making domains

Ability to understand and communicate quantitative signals and technical metrics in user-friendly ways

Experience integrating with analytical systems and understanding their outputs (statistical measures, risk metrics, validation results)

Understanding of how to structure and retrieve information from heterogeneous data sources

Experience building systems that cite sources and maintain data provenance

Willingness to develop deep domain expertise quickly

Engineering Excellence :

Ability to write production-quality code with appropriate testing and error handling

Experience with monitoring and observability for AI systems (tracking hallucinations, quality degradation, etc.)

Strong debugging skills for complex AI systems where failures are often subtle

Capacity to design systems that maintain quality while scaling to many concurrent users

Understanding of security considerations for AI systems (prompt injection, data leakage, etc.)

Experience building APIs and integration layers between different systems (AI models, databases, quant engines)

Ability to work cross-functionally with specialized teams (quants, data scientists, researchers) and integrate their outputs seamlessly

Product and Research Capabilities :

Ability to evaluate and help select appropriate technical approaches based on product requirements, cost constraints, and performance targets

Experience contributing to build‑vs‑buy decisions for ML components and helping justify architectural choices

Ability to translate user needs into technical requirements for AI systems

Experience designing conversational experiences that guide users effectively

Skill in evaluating subjective AI outputs and establishing quality metrics

Understanding of when to build custom solutions versus when to use existing LLM APIs

Capacity to stay current with rapidly evolving LLM research and apply relevant techniques

To apply for this position please send your CV to Don Fletcher via or contact Don via whatsapp on

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