At a Glance
- Tasks: Design and build innovative AI solutions that enhance automation and user experience.
- Company: Join a leading tech firm at the forefront of AI innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic work environment with strong focus on collaboration and career advancement.
- Why this job: Be part of a team shaping the future of AI with cutting-edge technologies.
- Qualifications: Degree in Computer Science or related field; experience in AI/ML solutions required.
The predicted salary is between 80000 - 100000 £ per year.
As an Applied AI/ML Lead Engineer in our Applied AI ML - Python & Agentic AI team, you will design, build, and productionize Generative AI and Agentic AI solutions. The ideal candidate brings a balanced mix of modern AI/ML delivery (LLMs/SLMs, RAG, tool-using agents, evaluation, MLOps) and backend/service engineering (Java and/or Python, APIs/microservices, testing, CI/CD, observability, reliability) on AWS and cloud-native platforms. This role values modern AI engineering workflows and tooling such as GitHub Copilot and Claude Code to accelerate delivery while maintaining quality and security. Familiarity with MCP (Model Context Protocol), Agent Skills and designing agentic systems that integrate models with tools and enterprise data via structured interfaces is a plus.
Job Responsibilities
- Design, develop, and deploy GenAI and Agentic AI solutions that improve automation, decision-making, and user experience across business workflows.
- Build LLM/SLM-powered applications including RAG-based systems, summarization/extraction pipelines, chat/coplay experiences, and tool-using agents.
- Engineer production-grade services using Java and/or Python (REST/gRPC APIs, microservices, libraries), following secure coding and reliability best practices.
- Develop prompt strategies and prompt engineering assets (templates, routing, guardrails), and implement automated evaluation to improve quality over time.
- Build and maintain data pipelines and processing workflows required for ML/GenAI use cases using cloud services.
- Apply MLOps practices across the lifecycle: experimentation, versioning, CI/CD, deployment, monitoring, and maintenance for models/prompts/agents.
- Implement robust testing (unit/integration), performance benchmarking (latency/cost), and observability (logging/metrics/tracing) for AI services.
- Collaborate with cross-functional stakeholders to define requirements, success metrics, and rollout plans; communicate complex topics clearly to technical and non-technical audiences.
- Strong problem-solving skills and ability to work effectively in ambiguous environments with multiple stakeholders.
Required Qualifications, Capabilities, and Skills
- Undergrad or Master’s degree (or equivalent practical experience) in Computer Science, Data Science, Machine Learning, or related field.
- Hands-on experience building applied AI/ML or GenAI solutions (e.g., RAG, classification, extraction, ranking, summarization, copilots).
- Familiarity with MCP (Model Context Protocol), Agent Skills and architectures that connect models to tools/data through standardized interfaces.
- Familiarity with LLM application patterns: embeddings/vector search, prompt orchestration, tool calling/function calling, safety/guardrails, evaluation.
- Strong software engineering experience delivering production systems; ability to design maintainable architectures and write clean, testable code.
- Proficiency in Java and/or Python and experience building APIs/services and integrating with data sources and downstream systems.
- Experience deploying solutions on AWS and cloud-native environments; understanding of security fundamentals and operational excellence.
- Experience with modern engineering practices: CI/CD, code reviews, unit testing (e.g., pytest/JUnit), and deployment automation.
- Experience with containers and orchestration (e.g., Docker, Kubernetes/EKS) and production monitoring practices.
Preferred Qualifications, Capabilities, and Skills
- Experience building agentic AI systems (multi-step workflows, tool routing, planning, memory patterns, supervision/fallback strategies).
- Experience with AWS Bedrock and/or SageMaker (or equivalent managed ML/GenAI platforms) and deployment patterns for scalable inference.
- Experience with evaluation frameworks and approaches (golden datasets, LLM-as-judge, human-in-the-loop review, red teaming).
- Experience fine-tuning models (e.g., LoRA/QLoRA/DoRA) and/or working with SLMs, embeddings, and retrieval systems.
- Experience with developer productivity tooling such as GitHub Copilot and Claude Code, paired with strong SDLC controls.
- Knowledge of the financial services industry and operating in regulated environments (auditability, controls, data handling).
- Exposure to distributed compute/training concepts (e.g., DDP, sharding) and performance/cost optimization.
Applied AI ML Lead - Python & Agentic AI in Glasgow employer: Jpmorgan Chase & Co.
As an Applied AI ML Lead Engineer, you will thrive in a dynamic and innovative environment that champions cutting-edge technology and fosters collaboration. Our company prioritises employee growth through continuous learning opportunities and encourages a culture of creativity and problem-solving, all while offering competitive benefits and a flexible work-life balance. Located in a vibrant tech hub, we provide access to a network of industry leaders and resources that empower you to make a meaningful impact in the field of AI.
StudySmarter Expert Advice🤫
We think this is how you could land Applied AI ML Lead - Python & Agentic AI in Glasgow
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI/ML projects. We want to see what you can do with Python and how you’ve tackled real-world problems. A strong portfolio can really set you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical skills and be ready to discuss your experience with LLMs, MLOps, and cloud services. We recommend practicing common interview questions and even doing mock interviews with friends.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Applied AI ML Lead - Python & Agentic AI in Glasgow
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your experience with AI/ML, Python, and any relevant projects you've worked on. We want to see how you can bring value to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about applied AI and how your background makes you a great fit for the role. Don’t forget to mention any specific projects or technologies you’ve worked with that align with what we do at StudySmarter.
Showcase Your Projects:If you've got any personal or professional projects related to AI/ML, make sure to include them in your application. We love seeing practical examples of your work, especially if they involve generative AI or agentic systems. It gives us a better idea of your hands-on experience!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining the StudySmarter team!
How to prepare for a job interview at Jpmorgan Chase & Co.
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python, Java, and AWS. Brush up on your knowledge of LLMs, RAG, and MLOps practices. Being able to discuss these topics confidently will show that you’re not just familiar but truly engaged with the role.
✨Showcase Your Problem-Solving Skills
Prepare examples from your past experiences where you tackled complex problems, particularly in AI/ML contexts. Use the STAR method (Situation, Task, Action, Result) to structure your answers. This will help you articulate your thought process and demonstrate your ability to navigate ambiguous situations.
✨Demonstrate Collaboration
Since this role involves working with cross-functional teams, be ready to discuss how you’ve successfully collaborated with both technical and non-technical stakeholders. Highlight any experiences where you communicated complex ideas clearly and effectively, as this is crucial for the position.
✨Prepare Questions That Matter
Have a list of insightful questions ready to ask your interviewers. Inquire about their current projects, team dynamics, or how they measure success in the role. This shows your genuine interest in the position and helps you assess if the company is the right fit for you.
