Lead Software Engineer - Python

Job Description
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Employee Platforms team, you serve as a seasoned member of an Incubation and Research team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.
This role requires a unique ability to apply state-of-the-art technical skills, work with ambiguity, and deliver projects to completion. You will be instrumental in developing innovative solutions, finding market fit, and delivering products that resonate with our users. As a hands-on engineer, you will bring in cutting-edge technologies, including Generative AI and ML.
Job responsibilities

• Execute software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
• Embrace ambiguity and lead the development of innovative solutions without a predefined roadmap.
• Implement the Build-Measure-Lean loop by rapidly prototyping, testing, and iterating on engineering solutions.
• Analyze user feedback and technical challenges to refine product offerings and ensure alignment with user needs.
• Create secure and high-quality production code and maintain algorithms that run synchronously with appropriate systems.
• Produce architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development.
• Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems. Proactively identify hidden problems and patterns in data and use these insights to drive improvements to coding hygiene and system architecture.
• Contribute to software engineering communities of practice and events that explore new and emerging technologies.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Hands-on practical experience in Python, SQL, advanced GenAI technologies like multimodality (Voice & Images), Agentic AI and ML technologies
• Highly proficient in coding in one or more languages such as Python, SQL, Java and R programming languages Experience with one or more platform tech stacks such as AWS, Docker, Kubernetes, Data bricks and CI/CD pipelines.
• Solid understanding of using ML techniques specially in Natural Language Processing (NLP), Knowledge Graph and Large Language Models (LLMs)
• Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search)
• Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
• Overall knowledge of the Software Development Life Cycle Solid understanding of agile methodologies such as CI/CD, application resiliency, and security

Preferred qualifications, capabilities, and skills

• Proficiency in optimizing and tuning AI models to ensure efficient, scalable solutions, with experience in building and deploying ML models on cloud platforms such as AWS and using tools like Sagemaker and EKS.
• Knowledge of data engineering practices to support AI model training and deployment, along with a strong understanding of machine learning algorithms and techniques-including supervised, unsupervised, and reinforcement learning-and hands-on experience with libraries such as TensorFlow, PyTorch, Scikit-learn, and Keras.
• Skills in collaborating with cross-functional teams to integrate generative AI solutions into broader business processes and applications, leveraging advanced LLM techniques such as Agents, Planning, and Reasoning.
• In-depth understanding of embedding-based search/ranking, recommender systems, graph techniques, and other advanced methodologies to enhance AI solution capabilities.