At a Glance
- Tasks: Design and build innovative GenAI and Agentic AI solutions to 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 transformative team shaping the future of AI technology.
- 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.
GenAI & Agentic AI Lead — Python in Glasgow employer: Jpmorgan Chase & Co.
Join our dynamic team as a GenAI & Agentic AI Lead in a collaborative environment that champions innovation and excellence. We offer competitive benefits, a strong focus on employee growth through continuous learning opportunities, and a culture that values creativity and teamwork. Located in a vibrant tech hub, you'll have access to cutting-edge resources and a network of like-minded professionals dedicated to pushing the boundaries of AI technology.
StudySmarter Expert Advice🤫
We think this is how you could land GenAI & Agentic AI Lead — Python in Glasgow
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your GenAI and Agentic AI projects. This is your chance to shine and demonstrate what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on common questions related to AI/ML and coding. Practice makes perfect, so consider mock interviews with friends or mentors.
✨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!
We think you need these skills to ace GenAI & Agentic AI Lead — Python in Glasgow
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience with GenAI and Agentic AI solutions. We want to see how your skills align with the job description, so don’t hold back on showcasing your relevant projects!
Show Off Your Technical Skills:Since this role is all about Python and AI/ML, be sure to include specific examples of your work with these technologies. Mention any projects where you’ve built APIs or worked with cloud services like AWS, as this will really catch our eye.
Keep It Clear and Concise:While we love detail, make sure your application is easy to read. Use bullet points for your achievements and keep your language straightforward. This helps us quickly see why you’d be a great fit for the team!
Apply Through Our Website:We encourage you to submit your application through our website. It’s the best way to ensure it gets into the right hands and allows us to track your application easily. Plus, it shows you’re keen to join us at StudySmarter!
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 and Java. Brush up on your knowledge of LLMs, RAG systems, and MLOps practices. Being able to discuss these topics confidently will show that you’re not just familiar with them but can also apply them effectively.
✨Showcase Your Problem-Solving Skills
Prepare examples from your past experiences where you tackled complex problems, especially in ambiguous environments. Highlight how you collaborated with cross-functional teams to define requirements and success metrics. This will demonstrate your ability to navigate challenges and work well with others.
✨Demonstrate Your Engineering Practices
Be ready to discuss your experience with CI/CD, unit testing, and deployment automation. Share specific instances where you implemented best practices in software engineering, such as writing clean, testable code or using containers like Docker. This will illustrate your commitment to quality and reliability in production systems.
✨Prepare for Technical Questions
Expect technical questions related to AI/ML solutions and backend engineering. Practice explaining concepts like prompt engineering, API design, and cloud-native deployments clearly. Being able to communicate complex ideas simply will impress both technical and non-technical interviewers.
