Senior Machine Learning Engineer

About The Job
You’ll work directly with the Head of AI Transformation to design, build, deploy and operate our internal AI platform. The scope is broad and designed to grow with you. In this role you will:
Build the platform’s foundation layers: retrieval and knowledge access over internal systems, an agent runtime and registry, and a governed data-access layer.
Build and operate the deployment platform that ships AI tools to business functions: scaffolding, CI/CD with built-in security checks, risk-tiered deploys, monitoring and rollback.
Build agentic applications and internal automation on top of the platform that streamline engineering and business workflows.
Favour open standards and reusable interfaces: assemble managed or open-source components for commoditised parts, and build only what is genuinely differentiated for us.
Own the end-to-end lifecycle of ML and agent microservices: planning, design, implementation, deployment and monitoring, in partnership with fellow engineers.
Write clean, efficient, reusable and maintainable code, and take ownership of existing codebases, systems and workflows.
Default to security and change-management thinking given the fintech, employee-data context, and partner closely with InfoSec, product, data scientists and business functions to define requirements.
Communicate clearly, translating complex technical concepts for both technical and non-technical audiences.
Grow with the role: as the platform and team mature, take on broader ownership across architecture, new capabilities and technical direction.

Additional scope: Senior MLE:
Drive architecture and technical direction across the platform’s layers and shared foundations (auth, integration interfaces, observability, deploy pipeline), built once and reused everywhere.
Guide and mentor engineers, and promote a culture of engineering excellence and continuous learning.
Lead planning and delivery of medium-to-large builds, and drive team productivity with sustainable development practices.

About You
Must have:
Bachelor’s degree in Computer Science or equivalent practical experience.
Backend engineering experience: 7+ years including technical leadership.
3+ years of hands-on Python.
Strong with Flask/FastAPI, REST APIs, SQL, and at least one cloud (AWS/GCP).
Message queues (RabbitMQ, Kafka or SQS), Docker, and modern CI/CD practices.
Production monitoring and observability (e.g. Datadog, Grafana): tracking latency, errors and alerts on scalable backend systems.
Hands-on experience building LLM and agentic applications: RAG or retrieval pipelines, tool-calling and agent orchestration, and prompt engineering.
Excellent communication and cross-functional collaboration skills.
Proven leadership planning and delivering medium-to-large software projects, plus mentoring.

Good To Have
Scaling chat-like platforms beyond 1,000+ active users: deep understanding of the architecture, performance and reliability challenges at that scale.
Deploying production-ready LLMs (via API and/or self-hosted models), with a solid grasp of observability, agentic concepts, memory, and token/prompt caching.
Streaming responses from backend to frontend: token streaming over Server-Sent Events (SSE), WebSockets or HTTP chunked transfer, delivering LLM output to the UI for responsive, real-time experiences.
Modern agent integration standards (e.g. Model Context Protocol) and building or integrating tool and agent interfaces.
Vector databases (pgvector, Pinecone, Weaviate) and embeddings.
Agent and LLM frameworks (LangGraph, LlamaIndex or similar) and enterprise search / RAG stacks.
Deploying ML models in production.
Integrating with enterprise tools and internal knowledge bases.
Experience in fintech or another regulated, security-sensitive environment.
Startup experience.