Senior Lead Software Engineer- Gen AI and AIML

J.P. Morgan; Glasgow; On-site; GBP 80,000 - 100,000; Full time

Job summary

A global financial services firm in Glasgow seeks a Senior Manager of Software Engineering to lead technical teams in implementing AI/ML solutions. This role involves designing sophisticated machine learning applications and mentoring engineers to optimize generative models for real-world applications. The ideal candidate will possess extensive experience in AI development, cloud computing, and strong interpersonal skills for cross-functional collaboration. This position offers the opportunity to make a significant impact in innovative financial technologies.

Qualifications

• Strong collaboration skills to work effectively with cross-functional teams.
• Experience in applied AI/ML engineering with a track record in production.
• Solid understanding of machine learning fundamentals and generative model architectures.

Responsibilities

• Design, develop, and deploy AI/ML solutions to meet business objectives.
• Mentor and guide a team of ML and MLOps engineers.
• Implement optimization strategies for generative models in NLP.

Skills

Leadership in technical coaching

Collaboration with cross-functional teams

Programming in Python

Experience with AI/ML engineering

Familiarity with cloud computing

Understanding of generative models

Education

Bachelor's or Master's in Computer Science or Engineering

Tools

TensorFlow

PyTorch

Docker

Kubernetes

OpenAI API

Job description

When you mentor and advise multiple technical teams and move financial technologies forward, it’s a big challenge with big impact. You were made for this.

As a Senior Manager of Software Engineering at JPMorganChase within the CORPORATE TECHNOLOGY, you serve in a leadership role by providing technical coaching and advisory for multiple technical teams, as well as anticipate the needs and potential dependencies of other functions within the firm. As an expert in your field, your insights influence budget and technical considerations to advance operational efficiencies and functionalities.

Job responsibilities

• Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases.
• Design, develop, and deploy state‑of‑the‑art AI/ML/LLM/GenAI solutions to meet business objectives.
• Manage, mentor, and guide a team of ML and MLOps engineers.
• Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency.
• Implement optimization strategies to fine‑tune generative models for specific NLP use cases, ensuring high‑quality outputs in summarization and text generation.
• Conduct thorough evaluations of generative models (e.g., GPT‑4.1), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications.
• Implement monitoring mechanisms to track model performance in real‑time and ensure model reliability.
• Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non‑technical audiences.
• Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting‑edge techniques, and leverage external APIs for enhanced functionality.

Required qualifications, capabilities, and skills

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field
• Experience in applied AI/ML engineering, with a track record of developing and deploying business‑critical machine learning models in production.
• Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API.
• Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit‑learn, and OpenAI API.
• Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization.
• Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs.
• Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine‑tune models for optimal performance in NLP applications.
• Strong collaboration skills to work effectively with cross‑functional teams, communicate complex concepts, and contribute to interdisciplinary projects.
• A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering.

Preferred qualifications, capabilities, and skills

• Familiarity with the financial services industries.
• Expertise in designing and implementing pipelines using Retrieval‑Augmented Generation (RAG).
• Hands‑on knowledge of Chain‑of‑Thoughts, Tree‑of‑Thoughts, Graph‑of‑Thoughts prompting strategies.