Senior Lead Software Engineer - Python, Data, AIML, Cloud at JPMC Candidate Experience page

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

19 May, 26

Salary

0.0

Posted On

18 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Data Engineering, AIML Engineering, Cloud, System Design, Application Development, Testing, Microservices, Distributed Systems, Data Intensive Applications, Infrastructure As Code, Containerized Application Development, Big Data, MLOps, ETL, DevOps

Industry

Financial Services

Description

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Senior Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank's Markets Research Technology team, you serve as a seasoned member of an agile 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. You will work on challenging Cloud-native data, backend engineering and AIML engineering, helping us industrialize AI/ML models at Production scale. This role is a technical hands-on Engineering role. Experience with data science/ML modeling is advantageous but not essential to this role. Job responsibilities Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development Builds engineering stack required for Data and AIML products, including data engineering, backend engineering, Cloud infra DevOps and MLOps Designs and implements data engineering solutions, leveraging modern big data technologies Contributes to software engineering communities of practice and events that explore new and emerging technologies Embraces a passion for learning, problem-solving, creative thinking and a can-do attitude. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years applied experience Hands-on practical experience in system design, application development, testing, and operational stability Proficient in coding in one or more languages- Python/Java 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 Proven track record in system design, architecting and developing microservices, distributed systems and data-intensive applications Experience with Cloud services, Infrastructure as Code, containerized application development, big data and modern data engineering technologies Practical experience developing Production-scale Cloud-native data engineering solutions in commercial environments Familiarity with Cloud Data engineering services (e.g., ETL, Glue, S3, Athena, RedShift) and MLOps stack Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds Preferred qualifications, capabilities, and skills Experience with data, AWS and AIML engineering in commercial settings, preferably in financial sector Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases Prior experience collaborating with data scientists

Responsibilities

The engineer will design and deliver trusted, market-leading technology products within an agile team, focusing on secure, stable, and scalable solutions across various business functions. Responsibilities include executing software solutions, creating high-quality production code, producing architecture artifacts, and building the engineering stack for Data and AIML products.