JPMorgan Senior Lead Software Engineer - AI Engineering 

United States, California, Palo Alto 

553064993

Job responsibilities

• Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
• Develops secure and high-quality production code, and reviews and debugs code written by others
• Drives decisions that influence the product design, application functionality, and technical operations and processes
• Serves as a function-wide subject matter expert in one or more areas of focus
• Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
• Influences peers and project decision-makers to consider the use and application of leading-edge technologies
• Adds to the team culture of diversity, equity, inclusion, and respect
• Deploy LLMs, Vector datastores, and RAG systems as a service with scalability and security requirements
• Design, implement and deploy scalable data pipelines on distributed compute platforms
• Deploy and operate Generative AI benchmarking systems to advance the firm's understanding of various LLM and Agentic system capabilities
• Evaluate Generative AI Products and procure into JPMC following proper firmwide standards and control policies

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering* concepts and 5+ years applied experience
• Hands-on practical experience delivering system design, application development, testing, and operational stability
• Advanced in one or more programming language(s): Python, Go, Javscript, Java
• Advanced knowledge of software applications and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
• Ability to tackle design and functionality problems independently with little to no oversight
• Practical cloud native experience
• Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field
• Highly capable with Kubernetes, Helm, Docker and container based application deployments
• Strong experience with enabling and executing distributed compute for data engineering and AI/ML training/fine tuning
• Experience with AI/ML Ops tools, experiment tracking, model lifecycle and governance best practices
• Experience with open-source frameworks: Ray, Spark, PyTorch, LlamaIndex, LangChain

Preferred qualifications, capabilities, and skills

• Experience with Vector datastores, Chroma, Elastic, DeepLake
• Some experience with NLP projects using prompt engineering, prompt based learning, Chain-Of-Thought techniques
• Knowledge of various LLM fine tuning techniques: SFT, RLHF, DPO, Lora, Quantization