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**Senior Lead Software Engineer - Java/Python/AWS/LLM** Spearhead AWS cloud and AI/ML initiatives, driving end-to-end delivery of complex Java & Python projects. Lead team developing cloud-native microservices, APIs, and AI-driven capabilities, leveraging Large Language Models (LLMs). Define architecture, set tech direction, and enforce engineering standards. Experience required: 12+ years building production systems, 7+ years leading complex initiatives. Key skills: AWS, cloud-native, microservices, RESTful APIs, AI/ML, LLMs.

Compensation: Not specified
City: Not specified
Country: United States

Full Job Description

Location: Plano, TX, United States

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorganChase within the Consumer & Community Banking, OPS Tech, 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job responsibilities:

Lead end-to-end delivery of complex full-stack initiatives from concept through production launch, balancing speed, quality, risk, and maintainability.
Set technical direction for the team/domain: define target architecture, drive roadmap-level technical decisions, and align designs to business priorities and constraints.
Architect and deliver cloud-native microservices and APIs with strong security controls, scalability, resilience, and performance; ensure designs are operationally production-ready.
Lead development of AI-driven capabilities, including LLM-based services, orchestration, and integrations into business workflows; establish patterns that are reusable across squads.
Drive team adoption of enterprise-authorized AI-assisted engineering practices (e.g., CoPilot, Claude) to improve code quality, delivery speed, and operational outcomes; set consistent validation standards (secure coding, peer review, automated testing) and promote reuse.
Establish and enforce engineering standards across the team (coding standards, API guidelines, testing strategy, dependency management, documentation expectations, and operational readiness).
Own reliability outcomes for key services: define SLIs/SLOs, lead incident response and RCA/postmortems, and drive continuous improvements to stability, latency, and cost.
Lead observability and operational automation strategy (logs/metrics/tracing, alert quality, runbooks, auto-remediation) to reduce recurring issues and improve MTTR.
Drive CI/CD maturity and safe delivery practices (quality gates, canary/blue-green, feature flags, rollback strategy), enabling predictable releases and zero/low downtime changes.
Mentor and grow engineers (senior and junior) through technical coaching, architecture reviews, and feedback; cultivate a culture of technical excellence and continuous improvement.
Communicate status, risks, and tradeoffs clearly across stakeholders; manage priorities and dependencies across multiple initiatives while operating effectively in Agile/Scrum.

Required qualifications, capabilities, and skills:

Formal training or certification on software engineering concepts and 5+ years applied experience in addition to 7+ years of applied experience building production systems, including leading delivery of complex initiatives.
Strong system design, application development, and operational stability skills in production environments, with demonstrated ownership of on-call readiness and incident outcomes.
Expertise designing and building microservices and RESTful APIs, with strong knowledge of data technologies and integration patterns, and ability to set standards used by others.
Hands-on experience building cloud-native systems on at least one major cloud platform (AWS, GCP, or Azure), including production deployment and operationalization.
Experience with distributed systems concepts (scalability, resiliency, concurrency, idempotency, failure modes, and performance tuning) and ability to guide design reviews.
Proven ability to influence product design and technical direction by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment, with demonstrated ability to evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Demonstrated experience leading effective team use of approved AI-assisted development practices (coding, code review, test acceleration, troubleshooting), including establishing team expectations and reusable validation patterns.
Hands-on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, summarization/extraction, workflow assistants), including evaluation and monitoring considerations.
Understanding of responsible AI use in engineering workflows (data sensitivity, safe handling of inputs/outputs, resiliency/security expectations) and ability to guide peers on safe and effective usage.
Strong communication and stakeholder management skills; ability to drive alignment, resolve ambiguity, and deliver outcomes across multiple initiatives.

Preferred qualifications, capabilities, and skills:

AWS cloud certification(s).
AI/ML or GenAI certification(s) (or equivalent practical experience delivering LLM-enabled capabilities).
Advanced Java proficiency (primary) and working knowledge of Python.
Experience with Docker, Kubernetes, and modern CI/CD practices.
Experience with multi-region deployments, disaster recovery, and zero-downtime release patterns.
Strong understanding of responsible AI practices, including prompt/agent safety patterns and secure handling of sensitive data.
Track record delivering scalable, reliable, and secure products from concept to launch i
n a regulated environment.
Domain familiarity with financial services technology; IVR/CCaaS experience a plus.

Drive design and delivery of highly extensible customer features across a diverse group of businesses. Spearhead the agentic AI transformation of customer voice experiences.

Full Job Description

Location: Plano, TX, United States

Lead end-to-end delivery of complex full-stack initiatives from concept through production launch, balancing speed, quality, risk, and maintainability.
Set technical direction for the team/domain: define target architecture, drive roadmap-level technical decisions, and align designs to business priorities and constraints.
Architect and deliver cloud-native microservices and APIs with strong security controls, scalability, resilience, and performance; ensure designs are operationally production-ready.
Lead development of AI-driven capabilities, including LLM-based services, orchestration, and integrations into business workflows; establish patterns that are reusable across squads.
Drive team adoption of enterprise-authorized AI-assisted engineering practices (e.g., CoPilot, Claude) to improve code quality, delivery speed, and operational outcomes; set consistent validation standards (secure coding, peer review, automated testing) and promote reuse.
Establish and enforce engineering standards across the team (coding standards, API guidelines, testing strategy, dependency management, documentation expectations, and operational readiness).
Own reliability outcomes for key services: define SLIs/SLOs, lead incident response and RCA/postmortems, and drive continuous improvements to stability, latency, and cost.
Lead observability and operational automation strategy (logs/metrics/tracing, alert quality, runbooks, auto-remediation) to reduce recurring issues and improve MTTR.
Drive CI/CD maturity and safe delivery practices (quality gates, canary/blue-green, feature flags, rollback strategy), enabling predictable releases and zero/low downtime changes.
Mentor and grow engineers (senior and junior) through technical coaching, architecture reviews, and feedback; cultivate a culture of technical excellence and continuous improvement.
Communicate status, risks, and tradeoffs clearly across stakeholders; manage priorities and dependencies across multiple initiatives while operating effectively in Agile/Scrum.

Required qualifications, capabilities, and skills:

Formal training or certification on software engineering concepts and 5+ years applied experience in addition to 7+ years of applied experience building production systems, including leading delivery of complex initiatives.
Strong system design, application development, and operational stability skills in production environments, with demonstrated ownership of on-call readiness and incident outcomes.
Expertise designing and building microservices and RESTful APIs, with strong knowledge of data technologies and integration patterns, and ability to set standards used by others.
Hands-on experience building cloud-native systems on at least one major cloud platform (AWS, GCP, or Azure), including production deployment and operationalization.
Experience with distributed systems concepts (scalability, resiliency, concurrency, idempotency, failure modes, and performance tuning) and ability to guide design reviews.
Proven ability to influence product design and technical direction by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment, with demonstrated ability to evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Demonstrated experience leading effective team use of approved AI-assisted development practices (coding, code review, test acceleration, troubleshooting), including establishing team expectations and reusable validation patterns.
Hands-on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, summarization/extraction, workflow assistants), including evaluation and monitoring considerations.
Understanding of responsible AI use in engineering workflows (data sensitivity, safe handling of inputs/outputs, resiliency/security expectations) and ability to guide peers on safe and effective usage.
Strong communication and stakeholder management skills; ability to drive alignment, resolve ambiguity, and deliver outcomes across multiple initiatives.

Preferred qualifications, capabilities, and skills:

AWS cloud certification(s).
AI/ML or GenAI certification(s) (or equivalent practical experience delivering LLM-enabled capabilities).
Advanced Java proficiency (primary) and working knowledge of Python.
Experience with Docker, Kubernetes, and modern CI/CD practices.
Experience with multi-region deployments, disaster recovery, and zero-downtime release patterns.
Strong understanding of responsible AI practices, including prompt/agent safety patterns and secure handling of sensitive data.
Track record delivering scalable, reliable, and secure products from concept to launch i
n a regulated environment.
Domain familiarity with financial services technology; IVR/CCaaS experience a plus.

Drive design and delivery of highly extensible customer features across a diverse group of businesses. Spearhead the agentic AI transformation of customer voice experiences.

Senior Lead Software Engineer - Java/Python/AWS/AI/LLM

Full Job Description

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