Develops secure, scalable software and data solutions using Databricks, Spark/PySpark, AWS, Python, and SQL. Designs applications, optimizes large-scale data processing, builds production ETL/ELT systems and data models, and supports AI/ML pipelines. Creates architecture artifacts, troubleshoots complex systems, validates AI-assisted development outputs, improves coding standards, and mentors engineers. Preferred experience includes Delta Lake, Unity Catalog, streaming, Airflow, MLflow, infrastructure as code, containers, Kubernetes, and monitoring tools.
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Software Engineer III at JPMorganChase within the Commercial & Investment Bank Payments 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.
Job responsibilities
As a Software Engineer III at JPMorganChase within the Commercial & Investment Bank Payments 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.
Job responsibilities
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
- Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
- Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
- Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability - Strong hands-on expertise in Databricks and Spark/PySpark, including deep performance optimization experience.
- Strong hands-on experience with AWS cloud-native implementation (storage, compute, IAM/security, networking) and production engineering practices.
- Strong proficiency in Python and SQL, including advanced query design, optimization, and data validation.
- Proven experience building and managing data models and operating production-grade ETL/ELT systems.
- Demonstrated AI/ML experience (supporting model training/inference, feature engineering pipelines, or MLOps practices).
- Proven leadership/SME capability: mentoring, driving engineering standards, and making sound technical decisions based on business needs.
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
- Overall knowledge of the Software Development Life Cycle
Preferred qualifications, capabilities, and skills
- Familiarity with modern front-end technologies
- Exposure to cloud technologies
- Experience with Delta Lake, Unity Catalog, Structured Streaming, and orchestration tools (e.g., Airflow)
- Familiarity with MLflow, feature stores, model serving, or LLM/GenAI-enabled applications
- Experience with IaC (Terraform/CloudFormation), Docker/Kubernetes, and monitoring/logging toolsets
JPMorganChase Mumbai, Maharashtra, IND Office
B11, B9, B10, Nirlon Knowledge Park, near Mrinal Tai Gore Flyover, Cama Industrial Estate, Mumbai, Maharashtra, India, 400063
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