Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Technical Lead - Software Engineer (Full Stack) Bristol

Bristol
10 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Head of Data Engineering - Hybrid, Scale & Data Excellence

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer...

Job Title: Technical Lead (Software Engineer) (Full stack)- Python, PySpark, HPC
Salary: £100,000 - £130,000 | Location: Bristol, UK (Hybrid)

About the Company

My client is a pioneering InsurTech specialising in cyber reinsurance, delivering advanced analytic's and underwriting solutions that transform cyber risk management. Their skilled, collaborative team thrives on data science and engineering innovation.

The Role: Technical Lead

We're seeking a hands-on Technical Lead to drive platform development, build and lead a high-performing engineering team, and integrate advanced risk modelling into their cyber reinsurance platform. This role requires 10+ years' experience in software engineering, including 5+ years in leadership, preferably in insurance or financial services.

Key Responsibilities

Platform Development: Architect and develop acyber reinsurance platform, incorporating:
Reinsurance submission ingestion, policy administration, cyber risk modelling, portfolio optimisation, and advanced reporting.
Team Leadership: Build and manage a high-performance engineering team across HPC, data engineering, and web development.
Collaboration: Work closely with data science and modelling teams to integrate analytical models.
Scaling Strategy: Expand the platform across new business lines.
Hands-On Contribution: Remain actively involved in the codebase, solving technical challenges and mentoring the team.

Qualifications & Skills

10+ years in software engineering, you must be experienced across the Full stack both Front and Back End with 5+ years in leadership (preferably in insurance).
Strong Python and PySpark skills, plus HPC, large-scale data engineering, and full-stack development.
Experience with machine learning, cloud platforms (AWS, GCP, Azure), DevOps tools (Docker, Terraform, Kubernetes), and data lakehouses (Databricks).
Proven success in building and scaling engineering teams and aligning initiatives with business goals.

Why Join?

Lead a cutting-edge team in cyber reinsurance.
Shape the future of risk management with advanced analytics.
Work in a highly collaborative, innovative environment.

How to Apply

Send your CV to to explore this opportunity

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.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.