AI Engineer

Hydra X is hiring an AI Engineer to build and operate the AI systems used across compliance, client onboarding and operations. This is a hands-on engineering role. You will design, deploy and evaluate AI systems running in production inside a regulated financial institution.

Key responsibilities

• Build, deploy and optimise AI and machine learning systems in production, including for scale, latency and cost.
• Build pipelines for the extraction, transformation and loading of large volumes of unstructured data, including regulatory documents, client records and operational logs, into retrieval and inference workflows.
• Implement retrieval-augmented generation pipelines: document curation, category filtering, relevance scoring and citation controls, so that outputs are traceable to authoritative sources.
• Design multi-agent orchestration and human-in-the-loop routing for workflows subject to regulatory approval gates.
• Run experiments to test the performance of deployed models. Build and maintain evaluation frameworks covering task completion, error rates and human handoff rates, and failure-mode testing for hallucination, prompt injection and boundary violations.
• Diagnose and resolve defects arising in deployed systems.
• Work with the infrastructure on which models are deployed, including orchestration frameworks, vector stores, model APIs and cloud services.
• Implement guardrails, immutable audit logging and role-based access controls appropriate to a regulated environment.
• Work with compliance, operations, product and engineering colleagues to specify requirements and move systems from prototype into production.

Requirements

• Bachelor's degree in Computer Science, Information Technology, Programming and Systems Analysis, or Science (Computer Studies). Alternatively, a minimum of three years of work experience as an AI engineer, AI researcher or AI scientist.
• Demonstrated experience building and deploying AI systems into production, not prototypes alone.
• Proficiency in Python, with experience in orchestration frameworks such as LangGraph or CrewAI, vector databases, and commercial model APIs.
• Experience designing evaluation and failure-mode testing for AI systems.
• Working knowledge of cloud deployment, infrastructure as code and CI/CD.
• Experience delivering software under regulatory requirements is an advantage.