Senior Agentic AI – Harness Engineer

• Design, build, and operate production AI agents for financial operations and enterprise workflows
• Design, deploy, and support reliable production runtimes for AI agents
• Build and extend agent loops, including context assembly, retrieval, memory, tool selection, execution limits, workflow state, and response validation
• Enforce authorization, human approvals, policy checks, validation, checkpoints, idempotency, retries, and exception handling
• Build validated, auditable tool and system interfaces with least-privilege permissions and prompt-injection defenses
• Isolate model reasoning from deterministic execution through controls outside the model
• Build repeatable tests and regression suites for agent outcomes, supporting evidence, and tool-execution paths
• Diagnose quality, safety, failures, latency, and cost
• Monitor and troubleshoot production agents
• Maintain audit evidence and turn operational findings into reusable engineering standards
• Partner across engineering, security, data, and operations to move AI solutions into production
• Establish reusable patterns for HSFT engineering teams

• Bachelor’s degree in a relevant technical field or equivalent practical experience
• 5+ years of software engineering experience
• At least 1 year delivering LLM or agent-based applications in production
• Strong Python, API design, asynchronous processing, automated testing, source control, and AWS engineering skills
• Experience with a managed model platform such as Amazon Bedrock or an equivalent service
• Hands-on experience implementing agent loops, context assembly, retrieval, memory, tool selection and limits, durable state, checkpoints, retries, and idempotency
• Ability to build regression evaluations for outcomes, evidence, and tool paths
• Ability to enforce least-privilege tool access and prompt-injection defenses
• Ability to separate model reasoning from deterministic controls
• Strong troubleshooting, analytical, communication, and cross-functional collaboration skills
• Experience in financial services or another regulated or highly controlled environment preferred
• Experience with model or tool gateways and interoperability protocols such as MCP, plus multi-agent systems and durable workflow engines preferred
• Knowledge of secure tool execution, containers, infrastructure as code, CI/CD, and observability platforms preferred
• Familiarity with responsible AI, governance, model risk management, and regulatory controls preferred
• Experience building shared AI platforms, mentoring engineers, or establishing reusable engineering standards preferred
• Relevant certifications in AWS, cloud engineering, AI, security, or data preferred

• Flexible working arrangements
• Comprehensive health and well-being benefits
• Structured development programs
• Leadership opportunities and clear paths for growth
• Full private medical insurance
• Permanent health insurance/income protection
• Life insurance
• Employee Assistance Programme for employee and immediate family
• Defined contribution pension scheme with competitive employer contributions
• Competitive annual holiday entitlement
• Annual professional membership fee reimbursement
• Club/gym subsidy
• Discretionary annual bonus scheme
• Employee Referral Program
• Remote/hybrid working opportunities, or from the office
• Tuition support for relevant professional qualification exams, including paid study and exam leave
• Sports and Social Club