Senior Lead Software Engineer

144780-Payments Us
Glasgow City Centre
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Software Engineer - Agentic AI/Machine Learning

Data Scientist

Data Scientist

Data Scientist

Data Scientist / Software Engineer

Senior Full Stack Data Engineer (Client Facing)

Description Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products. As a Senior Lead Software Engineer at JPMorgan Chase within the Finance Data, Toolsets and Services (FDTS) team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications. Job responsibilities Leads and owns technical deliveries, driving solutions through from inception through to production Develops Java/Python applications (hands-on) integrated with AWS services like EC2, S3, Lambda, DynamoDB etc. Drives decisions that influence the product design, application functionality, and technical operations and processes, experience working with Architects to get the best solution Regularly provides technical guidance and hands-on support across the team Acts as the guardian of quality through code reviews, driving secure, stable and scalable implementations Troubleshoots and optimises existing systems for security, performance, availability and cost Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors Develops secure and high-quality production code, and reviews and debugs code written by others Drives decisions that influence the product design, application functionality, and technical operations and processes Influences peers and project decision-makers to consider the use and application of leading-edge technologies Adds to the team culture of diversity, equity, inclusion, and respect Required qualifications, capabilities, and skills Formal training or certification on team leadership concepts and advanced applied experience Proven ability to Implement and advocate CI/CD pipelines for automated testing and deployment on AWS Practical hands-on experience in developing, debugging, optimising and maintaining code in a large corporate environment with one or more modern programming languages (Java or Python) and database querying languages Experience/knowledge of AWS Architectures including AWS serverless computing and Microservices Architecture Hands-on practical experience in leading system design, application development, testing, and operational stability Experience in working with dispersed global teams to influence and meet objectives Strong experience in writing secure, testable, maintainable code Ability/experience in solving data-oriented problems using multiple relevant technologies e.g. SQL, Relational DB, Spark, NoSQL while optimizing for performance Experience reviewing peer code, improving overall quality of deliveries across a team Strong troubleshooting and performance optimization skills, ability to manage Production issues. Practical experience of working in and driving an agile culture Preferred qualifications, capabilities, and skills Experience of Spark performance tuning of complex queries on large datasets Experience of Big Data technologies Experience of Databricks or Cloudera Ability to build Fullstack applications Exposure to working in an agile development (e.g. story pointing etc)

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.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

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.