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

Apply Now

Python Data Engineer III- Machine Learning

JPMorgan Chase & Co.
Glasgow
7 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Data Scientist III - Experimentation Science (Statistical Methodologies)

Python Data Engineer

Python Data Engineer - Hedgefund

Data Engineer (Python)

Lead Data Engineer (Data Science Team)

Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.


As a Data Engineer III at JPMorgan Chase within the Developer Platforms and Insights team, you serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

Design, develop, and deploy machine learning models to solve complex business problems. Collaborate with cross-functional teams to integrate ML models into production systems. Utilize PyTorch, Scikit-learn, NumPy, and Pandas for data analysis and model development. Develop and maintain APIs for model deployment and integration. Fine-tune large language models to enhance performance and accuracy. Apply deep learning architectures such as LSTMs and Transformers to relevant projects. Stay updated with the latest advancements in generative AI and implement innovative solutions. Conduct statistical analysis to support model development and validation.

Required qualifications, capabilities, and skills

Formal training or certification on Data engineering concepts and applied experience cProven experience in building and deploying machine learning models. Hands-on experience with PyTorch, Scikit-learn, NumPy, and Pandas. Proficient in Python programming language and building APIs. Solid understanding of statistics and machine learning theory. Experience with deep learning architectures, including LSTMs and Transformers. Experience in fine-tuning large language models. Knowledge of generative AI (GenAI) technologies. Strong problem-solving skills and the ability to work independently and collaboratively. Excellent communication skills to convey complex technical concepts to non-technical stakeholders.

Preferred qualifications, capabilities, and skillsExperience with cloud platforms such as AWS, Google Cloud, or Azure. Familiarity with version control systems like Git. Experience in deploying models using containerization technologies like Docker.

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.