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.