Senior Python/AI Engineer - LLM & Data Platforms

Socket.dev

New Jersey

On-site

USD 120,000 - 190,000

Full time

7 days ago

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Job summary

JPMorganChase is seeking a Software Engineer III focusing on Python/AI to join the Commercial and Investment Bank technology teams. You will contribute as a seasoned engineer to design and deliver scalable, secure products across the technology stack, collaborating within an agile environment.

You will build UI dashboards with React/TypeScript and Python services, design data pipelines on Databricks and Snowflake, and drive responsible AI integration throughout the SDLC with rigorous validation

Qualifications

Formal training or certification on software engineering concepts and 3+ years applied experience.
Strong Python skills, including familiarity with agentic development practices.
Experience with database design and data modeling on modern data platforms (Databricks, Snowflake).
Hands-on experience using approved AI-assisted development tools to design and deliver end-to-end applications, with strong validation habits for correctness, performance, and security.
Experience developing, debugging, and maintaining code in a large enterprise environment using one or more modern programming languages and database querying languages.
Strong understanding of SDLC and agile delivery practices, including CI/CD, application resiliency, and security controls.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs/outputs, and resiliency/security expectations.

Responsibilities

Build and maintain UI dashboards using React/TypeScript and backend services in the Python stack.
Design and deliver data pipelines/ETL on modern data platforms (e.g., Databricks, Snowflake).
Execute software design, development, testing, and technical troubleshooting across the SDLC.
Ensure large language models (LLMs) are used as controlled, well-understood components of the engineering lifecycle (enterprise-approved models).
Lead structured requirements analysis using LLM-assisted workflows to translate business and regulatory needs into clear technical specifications.
Establish best practices for prompt-driven design and development, treating prompts as versioned, reviewable engineering artifacts.
Ensure prompt strategies support determinism, reproducibility, traceability, and auditability in regulated environments.
Ensure LLM-driven systems meet enterprise reliability, resilience, and security expectations.
Coach teams on safe, compliant LLM/agent usage by documenting strengths, limitations, and risk profiles for different classes of engineering work.
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.