Applied AI ML Lead - LLM Suite Engineering

Applied AI ML Lead - LLM Suite Engineering

Full-Time 80000 - 100000 € / year (est.) Home office (partial)
Jpmorgan Chase & Co.

At a Glance

  • Tasks: Lead the development of cutting-edge AI/ML solutions and enhance the LLM Suite platform.
  • Company: Join JPMorganChase, a leader in applied AI innovation.
  • Benefits: Competitive salary, remote work options, and opportunities for professional growth.
  • Other info: Collaborative culture with a focus on learning and emerging technologies.
  • Why this job: Shape the future of intelligent systems and make a real impact in tech.
  • Qualifications: Degree in computer science or equivalent experience; proficiency in Python and system design.

The predicted salary is between 80000 - 100000 € per year.

Build what’s next in applied AI at JPMorganChase - where your work shapes how teams use intelligent systems at scale. You’ll lead hands‑on engineering for agentic and GenAI capabilities that power the LLM Suite platform. This role offers a mix of deep technical problem‑solving, architecture ownership, and collaboration with talented builders. If you enjoy turning ambiguity into reliable production systems, you’ll thrive here. Join a team that values craft, security, and learning.

As an Applied AI ML Lead in LLM Suite Engineering, you will design and deliver production‑grade AI/ML and agentic solutions that integrate seamlessly with existing systems. You will own technical direction across architecture, implementation, and operational stability, with a strong focus on secure, high‑quality software. You will partner with peers across engineering to identify patterns and improve standards, reliability, and scalability. You will help evolve the platform using modern public cloud services and agentic frameworks. You will contribute to a collaborative culture through communities of practice and emerging‑technology events. You will explore and operationalize emerging patterns such as agent‑to‑agent communication, model context protocols, and agentic orchestration, turning early‑stage concepts into scalable, production‑ready capabilities.

Job Responsibilities

  • Design, develop, and troubleshoot software solutions using creative approaches to solve complex technical challenges
  • Write secure, high‑quality production code and maintain algorithms that integrate with existing systems
  • Create architecture and design artifacts for complex applications, ensuring design constraints are met through delivery
  • Build AI/ML solutions and agentic systems for the LLM Suite platform using public cloud architecture (Azure, AWS) and modern agentic frameworks
  • Implement GenAI services leveraging Azure OpenAI models and AWS Bedrock
  • Identify hidden problems and patterns in data proactively to improve coding standards and system architecture
  • Participate in software engineering communities of practice and events focused on emerging technologies

Required Qualifications, Capabilities, and Skills

  • Computer science degree or equivalent practical experience
  • Hands‑on experience with system design, application development, testing, and operational stability
  • Proficiency in Python (FastAPI)
  • Experience building microservices and APIs
  • Experience with elastic compute, NoSQL databases, and messaging queues
  • Strong understanding of the Software Development Life Cycle
  • Solid grasp of CI/CD, application resiliency, and security

Preferred Qualifications, Capabilities, and Skills

  • Experience implementing GenAI services leveraging Azure OpenAI models and AWS Bedrock
  • Proficiency working with large language models and building agents with LangGraph
  • Experience developing, debugging, and maintaining code in a large corporate environment using modern programming and database querying languages
  • Experience with containerization
  • Knowledge of agent‑to‑agent (A2A) communication concepts
  • Familiarity with Model Context Protocol (MCP)
  • Experience with agentic orchestrators, personal AI assistants, or AI skills development

Applied AI ML Lead - LLM Suite Engineering employer: Jpmorgan Chase & Co.

At JPMorganChase, we pride ourselves on being an exceptional employer that fosters innovation and collaboration in the field of applied AI. Our work culture encourages continuous learning and professional growth, providing employees with opportunities to engage in cutting-edge projects while working alongside talented peers. With a strong emphasis on security, quality, and the use of modern cloud technologies, our team thrives in an environment that values creativity and technical excellence.

Jpmorgan Chase & Co.

Contact Detail:

Jpmorgan Chase & Co. Recruiting Team

StudySmarter Expert Advice🤫

We think this is how you could land Applied AI ML Lead - LLM Suite Engineering

Tip Number 1

Network like a pro! Reach out to folks in the industry, especially those at JPMorganChase. A friendly chat can open doors and give you insights that a job description just can't.

Tip Number 2

Show off your skills! Create a portfolio or GitHub repo showcasing your AI/ML projects. This is your chance to demonstrate your hands-on experience and problem-solving abilities in a tangible way.

Tip Number 3

Prepare for technical interviews by brushing up on system design and coding challenges. Practice makes perfect, so get comfortable with Python and the tools mentioned in the job description.

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, it shows you're serious about joining the team.

We think you need these skills to ace Applied AI ML Lead - LLM Suite Engineering

AI/ML Solutions Development
Agentic Systems Engineering
Public Cloud Architecture (Azure, AWS)
Python (FastAPI)
Microservices and APIs Development
NoSQL Databases
Messaging Queues

Some tips for your application 🫡

Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Applied AI ML Lead role. Highlight your hands-on engineering experience, especially in AI/ML and system design, to show us you’re the perfect fit!

Craft a Compelling Cover Letter:Use your cover letter to tell us why you're excited about this role at JPMorganChase. Share specific examples of how you've tackled complex technical challenges and contributed to collaborative projects in the past.

Showcase Your Technical Skills:Don’t forget to mention your proficiency in Python, experience with microservices, and any work with cloud services like Azure or AWS. We want to see how your technical expertise can help us build the next big thing in applied AI!

Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates during the process!

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, FastAPI, and cloud services like Azure and AWS. Brush up on your knowledge of microservices, APIs, and the Software Development Life Cycle to show you can hit the ground running.

Showcase Your Problem-Solving Skills

Prepare to discuss specific examples where you've tackled complex technical challenges. Think about how you turned ambiguity into reliable systems in past projects. This will demonstrate your hands-on engineering capabilities and your ability to think critically under pressure.

Emphasise Collaboration and Learning

Since this role involves working with talented builders, be ready to talk about your experiences in collaborative environments. Share how you’ve contributed to communities of practice or participated in tech events, as this shows you value teamwork and continuous learning.

Prepare for Technical Questions

Expect to dive deep into technical discussions during the interview. Brush up on your knowledge of agentic systems, model context protocols, and coding standards. Practising coding problems or system design scenarios can help you feel more confident when faced with technical questions.