Lead Python Engineer — Observability, AI & Data Pipelines

JPMorganChase

Houston (TX)

On-site

USD 140,000 - 190,000

Full time

14 days+

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Job summary

JPMorganChase in Houston seeks a Lead Software Engineer in Risk Corporate Technology to strengthen and deliver trusted, scalable technology products in a secure environment. You will be a core technical contributor, building critical solutions across multiple business functions in support of the firm’s objectives.

The role focuses on Python services, data/AI pipelines, AI-enabled workflows, and strong observability.

Qualifications

Formal training or certification on software engineering concepts and 5+ years applied experience.
Minimum of 8 years industry experience.
Strong Python engineering skills (data handling, APIs, concurrency basics, packaging).
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
Must have working knowledge in observability tools such as OTEL, Grafana, Splunk and Dynatrace.
Strong SQL skills (joins, window functions, query optimization, troubleshooting bad data).
Proven ability to debug quickly and work through ambiguous production issues.
Experience delivering production-grade software (testing, code reviews, version control).
Strong communication skills—can explain root cause and fixes clearly.
Demonstrated experience leading effective use of approved AI-assisted development tools and setting team expectations for validating AI outputs.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations and secure handling.

Responsibilities

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine approaches to build solutions or break down problems.
Build and maintain Python services, scripts, and pipelines for data/AI use cases.
Write efficient SQL for analysis, data validation, debugging, and performance tuning.
Rapidly triage incidents: reproduce issues, isolate root cause, and implement fixes.
Develop and iterate on AI applications (e.g., LLM-powered workflows, retrieval, evaluation).
Design and implement agentic systems (tool-using agents, orchestration, guardrails, memory patterns).
Create monitoring/observability: logging, metrics, traces, and alerting for AI and data services.
Drives team adoption of enterprise-authorized AI-assisted engineering practices, while establishing consistent validation standards and promoting reuse of patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle, including enterprise-authorized AI-assisted development and automation capabilities.
Document system behavior, known failure modes, and support procedures.