Lead Machine Learning Engineer, Crypto Platform

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 be the project lead and be the 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 :

LLM and RAG Expertise :

Technical Capabilities :

Data and Domain Knowledge :

Engineering Excellence :

Product and Research Capabilities :