Software Engineer - Applied AI

Millennium Management LLC

Charing Cross, United Kingdom

6 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Languages

English

Experience level

Senior

Charing Cross, United Kingdom

API

Artificial Intelligence

Airflow

Amazon Web Services (AWS)

Cloud Computing

Continuous Integration

Software Debugging

Document Management Systems

Distributed Systems

Python

Rapid Prototyping Process

Service Development Studio

Software Engineering

Systems Integration

Strategies of Testing

Data Logging

Large Language Models

Backend

Containerization

REST

Microservices

Collaboration: Work closely with AI Engineers to turn experimental notebooks, scripts, and workflows into reliable tools and services; co-design experiment-friendly systems (feature flags, prompts, model switches, eval hooks) that enable fast but safe iteration.
Architecture: Own the architecture of tooling and services, defining reusable templates, libraries, and patterns that balance rapid prototyping with maintainability and consistency across the team.
Observability: Lead observability for AI applications and pipelines-logging, metrics, tracing, alerting, and dashboards-so the team can quickly answer "what is happening right now?" in both experiments and production tools.
Reliability: Drive reliability and resilience practices for AI systems, including testing strategies, safe failure modes, rollout/rollback approaches, and standards for robust APIs that wrap AI/LLM functionality.
Infrastructure: Own cloud infrastructure for research tooling (e.g., AWS/GCP), including containerization, CI/CD, and infrastructure-as-code, while setting and upholding engineering standards for production-grade systems.
Knowledge Sharing: Document services and systems concisely and effectively, demo tools and code to the team, and create internal Agent tools/skills/playbooks the team can use to speed up development.

5+ years of professional software engineering experience with a strong backend or full-stack focus.
Experience integrating LLMs or other AI/ML systems into applications.
Deep experience building and operating production services end-to-end (design, implementation, deployment, monitoring, and incident response).
Strong proficiency with Python and modern service development (e.g., REST APIs, microservices).
Hands-on experience with observability stacks (logging, metrics, tracing, alerting) and debugging distributed systems in production.
Experience with workflow/orchestration tools (e.g., Airflow, Dagster, Prefect) and building reliable data or experiment pipelines.
Cloud deployment expertise (e.g., AWS/GCP), including containers, CI/CD, and infrastructure-as-code.
Comfortable working in ambiguous, research-oriented environments and translating loosely defined experimental code into maintainable, well-structured systems.
Strong communication and collaboration skills; able to coach prototype-focused engineers on production best practices and clearly explain tradeoffs to non-infrastructure stakeholders.