About the Job
Welcome to a global financial pioneer established in 2005, specializing in delivering cutting-edge trading technology, unparalleled liquidity, and exceptional customer service. Our extensive range of financial products includes Forex, Metals, Shares, Indices, Commodities, and Cryptocurrency CFDs.
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Join our thriving community of over 2 million clients across 100 countries, contributing to a daily trading volume exceeding US$ 35 billion. We are devoted to innovation, excellence, and empowering our clients to achieve their financial goals.
Role Overview
As the Lead Agentic AI Engineer within the Enterprise Data Platform (EDP), you will define and build the organization’s agentic AI capability while managing the end-to-end lifecycle of AI agents, from use-case discovery through to production monitoring. You will establish technical direction and standards while mentoring AI engineers, collaborating closely with data engineers and business leaders.
Key Responsibilities
Agentic AI Design & Development
- Design, build, and deploy single- and multi-agent systems that automate workflows across Sales, Marketing, Finance, Operations, and other business domains.
- Implement agent patterns including tool use, planning, memory, retrieval-augmented generation (RAG), and multi-agent orchestration.
- Build integrations to enterprise systems using APIs and Model Context Protocol (MCP) servers.
- Partner with business stakeholders to identify high-value use cases, define success metrics, and convert them into production agents.
Evaluation, Reliability & Safety
- Establish evaluation frameworks for agents covering task success, accuracy, regression testing, and latency benchmarks.
- Implement guardrails, human-in-the-loop checkpoints, and fallback behavior for sensitive workflows.
- Monitor agents in production for quality, drift, failure modes, and misuse while driving continuous improvement.
Cloud AI Platform Engineering
- Build and operate agent and LLM workloads on AWS (Bedrock, Lambda, ECS/EKS).
- Evaluate and integrate foundation models while considering quality and data residency requirements.
- Collaborate on scalability, identity and access, secrets management, and cost control.
LLMOps & Engineering Excellence
- Implement CI/CD, versioning for prompts, automated testing, and release practices for agent systems.
- Build reusable components and SDKs to enable consistent agent development.
- Ensure production-quality code, documentation, and runbooks.
Data Collaboration
- Collaborate with data engineers to consume curated datasets and expose governed data to agents.
- Define retrieval and knowledge strategies aligned with enterprise data models.
AI Governance, Security & Compliance
- Ensure agents comply with AI governance policy and regulatory requirements.
- Provide auditability, traceability, and explainability for agent actions.
- Contribute to the AI Governance Committee’s standards.
Technical Leadership
- Lead, mentor, and grow AI engineers; run design and code reviews.
- Define the agentic AI roadmap and communicate trade-offs to stakeholders.
Qualifications & Requirements
Experience & Education
- 8+ years in software engineering, ML, or AI engineering with 2+ years in LLM/agentic systems.
- Experience leading or mentoring engineers; technical lead experience preferred.
- Bachelor’s degree in Computer Science, Engineering, Mathematics, Data Science, or related field.
- Master’s degree or relevant certifications are a plus.
Agentic AI & LLM Engineering
- Proven experience building and deploying LLM-based agents in production.
- Hands-on with agent frameworks and protocols (e.g., LangChain, Claude/OpenAI agent SDKs).
- Strong prompt engineering skills and experience with LLM evaluation and observability tools.
AWS & Cloud
- Strong hands-on experience with AWS Bedrock and containerized deployments.
- Experience with SageMaker or other ML platform services is an advantage.
Programming & Open Source
- Expert Python; experience building APIs and services (FastAPI or similar).
- Familiarity with open-source ML/NLP libraries and ability to write clean, maintainable production code.
Data & Platforms
- Solid grounding in data pipelines, warehousing, and SQL; experience with Redshift or other platforms is a plus.
- Experience with data governance and metadata tooling is a plus.
Domain Experience
- Financial services, trading, FX, crypto, or regulated industry experience preferred.
Leadership & Behavioral Competencies
- Ownership mindset with the ability to deliver independently.
- Proven ability to lead and mentor engineers.
- Strong stakeholder management and ability to explain AI concepts to non-technical leaders.
Why Join Us?
- Build and lead agentic AI capability from the ground up with executive sponsorship.
- Own the full agent lifecycle in production with high-impact data.
- Work with an industry-leading global financial institution.
- Competitive salary and comprehensive employee benefits.
- Opportunities for professional growth and career advancement.
- Collaborative, inclusive, and dynamic work environment.
