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 70000 - 90000 € 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: JPMorganChase
As an Applied AI ML Lead Engineer, you will thrive in a dynamic and innovative environment that champions cutting-edge technology and fosters a culture of collaboration and continuous learning. Our commitment to employee growth is reflected in our comprehensive training programmes and opportunities to work on impactful projects that shape the future of AI solutions. Located in a vibrant tech hub, we offer competitive benefits, flexible working arrangements, and a supportive atmosphere that values creativity and diversity.
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 AI/ML space on LinkedIn or at meetups. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI/ML projects, especially those involving Python and cloud services. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on common AI/ML concepts and coding challenges. Practice explaining your past projects clearly, focusing on your problem-solving skills and technical expertise.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it’s a great way to ensure your application gets noticed.
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 hands-on experience with AI/ML solutions 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 AI and how your background makes you a great fit for the Applied AI ML Lead role. Don’t forget to mention any specific technologies or methodologies you’ve used that align with our needs.
Showcase Your Projects:If you've built any AI/ML applications or have relevant projects, make sure to include them in your application. We love seeing practical examples of your work, especially if they involve LLMs, RAG systems, or cloud deployments. It gives us a better idea of your capabilities!
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 JPMorganChase
✨Know Your AI Inside Out
Make sure you brush up on the latest trends in Generative AI and Agentic AI. Be ready to discuss your hands-on experience with LLMs, RAG systems, and any projects you've worked on that showcase your skills in building applied AI/ML solutions.
✨Showcase Your Coding Skills
Since this role requires proficiency in Python and/or Java, be prepared to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice writing clean, testable code and be familiar with APIs and microservices.
✨Understand MLOps Practices
Familiarise yourself with MLOps principles, including CI/CD, versioning, and monitoring. Be ready to discuss how you've implemented these practices in past projects, as they are crucial for ensuring the reliability and quality of AI services.
✨Communicate Clearly
This role involves collaborating with various stakeholders, so practice explaining complex AI concepts in simple terms. Think about how you can convey your ideas effectively to both technical and non-technical audiences during the interview.
