Software Engineer

Experis; Greater London; On-site; GBP 138,000 - 153,000; Full time

Job summary

Experis is seeking Software Engineers to strengthen our AI for Science (AI4S) team in London. You will design, build and operate scalable cloud infrastructure and services that support AI models and agentic systems, with 2–3 days on site.

This hands-on role focuses on production-grade, data-driven software to help scientists access clinical and biomedical data reliably. You will own CI/CD, automated testing, monitoring and incident response, work with ML engineers and domain experts, and deliver

Qualifications

• Proven experience building production-grade cloud-native software.
• Experience with AI/ML model serving in production.
• Strong Python development and modern cloud stacks.

Responsibilities

• Design, build and operate scalable cloud infrastructure for AI models.
• Own CI/CD, automated testing, monitoring and incident response.
• Collaborate with ML engineers and domain experts.
• Deliver robust, high-performance code in an agile environment.

Skills

Python

FastAPI

Cloud platforms

CI/CD

Testing

Kubernetes

PyTorch

TensorFlow

English fluency

Distributed systems

Tools

Terraform

GKE

Cloud Run

Cloud Storage

Artifact Registry

Cloud SQL

Job description

Software Engineer x2

Rate - £750 - £830

Location - London 2/3 days on site

Until end of July 2027

About the Role

To strengthen our AI for Science (AI4S) team, we are looking for Software Engineers with a track record in developing production‑grade, data‑driven software solutions. You will design, build and operate the scalable cloud infrastructure and services - including the serving of our models - that our AI systems and agentic applications run on, and you will be accountable for keeping them reliable in production. This is hands‑on software and platform engineering: building robust, well‑tested, high‑performance systems that scientists across the client nd on every day, on modern cloud technology and the vast biomedical data sources available to us.

In this role you will

• Design, build and operate scalable infrastructure and services that support our AI models and agentic systems across the entire software development life cycle.
• Own the reliability of what you build - set up CI/CD and release processes, automated testing, monitoring and alerting, and lead the response when things break, so the systems scientists rely on stay dependable.
• Build and operate the model‑serving infrastructure that exposes our models in production with efficient use of compute.
• Develop and maintain cloud‑native architectures that enable reliable deployment and scaling of AI/ML workloads.
• Deliver robust, tested and high‑performance code in an agile environment, and work closely with ML engineers and domain experts to make the infrastructure fit for purpose.

Qualifications & Skills

• Demonstrated advanced programming expertise in Python and in developing and delivering robust, scalable software solutions using frameworks like FastAPI.
• Experience with cloud platforms (GCP, Azure) and cloud‑native architectures.
• Passion for software design and commitment to the development of reusable, scalable, and testable software components.
• Basic understanding of at least one major deep learning framework (PyTorch, JAX, TensorFlow).
• Hands‑on experience with Google Cloud Platform, in particular the services we build on: Cloud Run, Google Kubernetes Engine, Cloud Storage, Artifact Registry, Cloud SQL.
• Fluency in English.Preferred Qualifications & Skills

If you have the following characteristics, it would be a plus:

• Familiarity with machine learning principles and state‑of‑the‑art modelling approaches.
• Experience in design, development and deployment of commercial cloud‑native software and infrastructure.
• Experience building and deploying large‑scale AI models and agentic systems in production environments.
• Experience architecting, developing, and deploying distributed training pipelines for large models with PyTorch or TensorFlow.
• Expertise in performance optimization, cost optimization, and efficient compute resource management in cloud environments.
• Experience running production services at scale, including defining and working to service‑level objectives (SLOs/SLIs).
• Experience with incident response and post‑incident review, and with building the observability that supports it.
• Infrastructure‑as‑code (e.g. Terraform) for provisioning and maintaining cloud environments.
• Experience developing and administering workloads on Kubernetes (e.g. GKE).
• Familiarity with GCP networking and security controls - VPC, VPC Service Controls (VPC‑SC), and private connectivity.
• Contributions to relevant open‑source projects.
• Knowledge or interest in disease biology, molecular biology and medicine.
• Experience working with biomedical data (e.g., genomics, transcriptomics, proteomics, electronic health records, clinical images).