Machine Learning demand

Skills needed:

a. Must have: Python, AWS,  experience in deploying and debugging machine learning models in production, RASA, experience with MLOPS, bitbucket/github

b. Preferred: Experience working with NLP models and conversational AI platforms

c. Years of experience: 8+ (Advanced)

Job description:

As a Machine Learning Software Engineer at JPMorgan Chase within the Consumer & Community Banking Machine Learning division, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems

• Develops secure high-quality production code, and reviews and debugs code written by others

• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems

• Adds to team culture of diversity, equity, inclusion, and respect

• Partner with other engineering teams, product, data scientists and analytics to discover opportunities to improve

• Be a part of our innovation and transformation roadmap

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years of applied experience

• Hands-on practical experience delivering system design, application development, testing, and operational stability

• Advanced experience in Python is required

• Solid experience with AWS

• Strong in developing machine learning models at scale from inception to business impact

• Hands-on experience in building and deploying machine learning models on a cloud platform

• Strong end-to-end working knowledge on how a machine learning product is built

• Familiarity with MLOPs and components in MLOPs ecosystem

• Good understanding of agile methodologies

• Excellent communication and presentation skills

Preferred qualifications, capabilities, and skills

• In-depth knowledge of the financial services industry and their IT systems

• Practical AWS cloud native experience

• Hands-on experience in building and optimizing conversational AI machine learning models

• Experience in data streaming tools such as Kafka

• Experience with Generative AI and LLMs (Large Language Models) are a plus

Specific day-to-day responsibilities:

• Be a part of agile ceremonies and standups

• Attend and contribute to tech design sessions

• Build scalable and reusable conversational dialog flows using python and RASA and deploy in production

• Automate model monitoring to catch data and model drift

• Debug performance issues and fine tune the code, bug fixes

• Write unit tests and documentation

• Follow and contribute to best practices