Senior Backend Engineer

Sprout.ai
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Backend Engineer – Train in Machine Learning – Full Remote

Senior Machine Learning Scientist - Search

Senior Data Scientist

Data Scientist

Senior MLOps Engineer

Senior Machine Learning Engineer, Gen AI

Salary banding:£74,000 - £104,000 dependent on experience

Working pattern:Hybrid; 1-2 days per week in the office

Location:London

About our Engineering Team

As a business which has AI at its core, we need to have a reliable, scalable and secure real-time ML platform to deliver our product to customers. The Engineering team makes this happen.

The team is UK-based, with a significant presence in London, and is made up of pragmatic, curious and collaborative problem-solvers who are passionate about working with our Data Scientists to build state of the art AI products. Our Software Engineers bring together a diverse range of expertise and backgrounds; what unites us is a desire to learn, a mastery of our discipline and strong technical prowess.

Our engineers are responsible for all aspects of the software development lifecycle. You will get the opportunity to work across our entire stack building features which deliver AI capabilities to some of the biggest names in the insurance industry.

We are developing a modern real-time ML platform using technologies like React, Typescript, PyTorch, Ray, k8s (helm + flux), Terraform, Postgres and Flink on AWS. We operate a fully Python stack except for frontend and infrastructure code. We are very big fans of Infrastructure-as-Code and enjoy Agile practices. 

As a team, we're driven by a relentless focus on delivering real value to customers at speed. We embrace modern engineering practices such as automated testing, continuous monitoring, feature flags, and on-demand production-like environments to support frequent, reliable releases.

Our team is tackling several exciting challenges, including:

  • Deploying all changes, including complex machine learning models, reliably to customers within 15 minutes
  • Building a real-time, configuration-driven platform that seamlessly adapts to diverse customer needs
  • Ensuring autoscaling and cost-efficient model serving in production, with robust support for ML monitoring and experimentation
  • Centralised reporting/metrics for both the business and our customers

Role Summary

We are looking for an engineer passionate about developer enablement and infrastructure as code, who is eager to expand their expertise by contributing to impactful product features. You'll play a key role in improving the lives of millions of insurance policyholders globally, working with a modern and powerful technology stack that includes:

  • Pythonfor application development
  • Terraformfor AWS infrastructure provisioning
  • Kubernetes(withHelmandFlux) for managing services
  • GitLabfor CI/CD and version control
  • AWSas our infrastructure platform
  • PostgreSQLfor application data and event sourcing architecture
  • Apache Flinkfor real-time service interactions and state management

Responsibilities

  • Work with different stakeholders across the business like engineers, product, engagement team to understand a problem space within your area, propose solutions, and own the end to end delivery of complex projects.
  • Own and maintain specific parts of our stack with best in class engineering practices.
  • Write comprehensive unit, integration and end-to-end automated tests in the backend for customer-facing features.
  • Lead on platform-facing work, using infrastructure-as-code (AWS, terraform, k8s) to ensure our platform is reliable and scalable.
  • Take a lead in code reviews, provide constructive feedback, and keep to date with latest trends in the industry.
  • Provide mentoring to other members of the Engineering and Data Science teams.
  • Lead in the continuous improvement of the processes and ways of working for the engineering team.
  • Manage feature rollouts with multiple releases per day by utilising feature flags, metrics, logs and alerting.
  • Champion the Engineering and Sprout company values

Requirements

  • Technical proficiency
    • Strong experience working in fully cloud-hosted environments (e.g., AWS) along with Infrastructure-as-Code frameworks (e.g., Terraform) and Kubernetes.
    • Strong proficiency in software architecture using Python or similar backend programming languages
    • Solid RDBMS experience, preferably with PostgreSQL
    • Experience building RESTful APIs (e.g. FastAPI) and real-time data processing pipelines
    • Bonus points for experience with Apache Flink and Flux
  • Deep understanding of modern software development lifecycles, including code quality, pull requests, code reviews, CI/CD, QA, and production releases in an agile, fast-paced environment
  • Strong problem-solving skills with the ability to think critically and creatively
  • Collaborative by nature, with excellent communication and teamwork abilities
  • Self-motivated, with a strong sense of ownership and accountability

Sprout.ai Values

Hungry for Growth - Unleash your inner Sprout: Sprouts embrace growth, forget comfort zones, and help Sprout.ai thrive.

Own It, Deliver It - We commit, we deliver, and we exceed expectations - it's how we achieve outstanding outcomes for our customers.

Seed Innovation - The future is shaped by those who dare to innovate. We embrace this mindset, planting the seeds for future growth, experimenting fearlessly and taking bold actions that unleash our ability to scale.

Collaborate to Blossom - We cultivate collaboration, working together to create a vibrant and diverse ecosystem where every Sprout can thrive. It drives better results, and creates a better environment for us all.

Engineering Values

In addition to our company-wide values, these are some of the values within the Engineering team that define how we work and grow together:

Value-Driven Development - we avoid premature optimisation and focus on delivering value to our customers based on known requirements.

Proactive Mindset - We embrace the philosophy of asking for forgiveness rather than permission, encouraging innovation and swift action.

⚡ Efficient Decision-Making - We optimise towards faster decision-making processes, distinguishing between reversible (two-way doors) and irreversible (one-way doors) decisions.

Equality of Opportunity - We strive to provide equality of opportunity for all team members, regardless of title or position, fostering a collaborative and inclusive environment.

Compensation, benefits and perks

  • Salary banding: £74,000 - £104,000 dependent on experience. Annual pay reviews.
  • Sprout.ai Share Options
  • 28 days’ annual leave (plus bank holidays)
  • Hybrid working with up to 4 days per week working from home
  • Private Health Insurance + Dental Insurance
  • Learning and Development budget
  • Monthly socials, both in London and Virtual
  • WeWork perks - barista, social events, snacks etc.
  • Macbook Pro + home working setup

About Sprout.ai

Sprout.ai was established in London, UK in 2018 with a mission to help people in their time of need when making an insurance claim. Inefficient claims processing for the insurer meant that customer experience was suffering and people were losing faith in their insurance policies. The average insurance customer was having to wait over 25 days to receive an outcome on their claim, often in times of vulnerability.

The barriers to rapid claims settlement were clear; understanding of unstructured data, complexity and volume of decision making, legacy systems and processes.

Sprout.ai’s patented claims automation platform solves these challenges, and has already delivered instant claims settlement on millions of insurance claims around the world. Our proprietary AI products can automate every step of the claims journey: extracting and enhancing relevant claims data, cross-checking this with policies and providing recommendations to conclude a claim in near real-time. Our tools are allowing claims handlers to spend more time with customers, where human touch and empathy can make the most difference to their customers.

Leading VCs saw our company vision to ‘make every claim better’ and have supported our growth journey. This includes our $11M Series A led by Octopus Ventures in 2021 and in total we have raised over $20M.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.