AIOps Lead Software Engineer

JPMorgan Chase & Co.•Plano, TX

About The Position

As a Lead Software Engineer at JPMorganChase within the Consumer & Community Banking Home Lending and Auto Technology team, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
5+ years in software engineering, SRE, DevOps, or platform engineering, with practical experience in AIOps or AI-driven automation
Experience with AIOps tools and frameworks for model deployment, monitoring, retraining, and lifecycle management, and automating operational tasks (e.g. incident triage, auto-remediation)
Hands-on experience with GenAI/LLM frameworks (RAG Architectures, vector databases)
Proficiency in at least one modern programming language (e.g., Python), with experience building and integrating GenAI models and APIs
Strong background in observability and monitoring—designing and implementing distributed tracing, logging, metrics, dashboards, and alerting systems, including AI-assisted diagnostics
Solid understanding of SRE/DevOps principles (SLA/SLOs, error budgets, MTTR, MTTD, Chaos Engineering), with a focus on leveraging GenAI to improve reliability and efficiency
Hands-on experience with public cloud platforms (AWS), including deploying and managing GenAI workloads
Practical experience implementing AIOps solutions using GenAI for anomaly detection, predictive alerting, automated incident response, root cause analysis, and self-healing systems
Ability to explain complex technical concepts to product managers, executives, and non-technical audiences.
Experience introducing AI/ML solutions into engineering teams (with limited prior AI experience) including establishing reusable patterns, providing technical direction, and coaching engineers through AI deliveries.

Nice To Haves

Background in mortgage technology, lending systems, or regulated financial services
Experience building self-healig systems, auto-remediation workflows, or predictive alerting at scale
Experience with Chaos Engineering and proactive resilience testing
AWS certifications (e.g., Solutions Architect, Machine Learning Specialty) or equivalent cloud certifications

Responsibilities

Design and deploy AIOps solutions and automation pipelines to proactively detect, diagnose, and resolve incidents; reduce MTTD/MTTR, prevent recurrences
Identify and systematically eliminate operational toil through automation targeting manual processes in incident response, deployments, testing, reporting, and routine operations
Execute and lead the design and development of AI software solutions that enhance reliability, scalability, and performance of the Loan Origination System (LOS) and related platforms, including the ability to think beyond routine or conventional approaches to build solutions or break down technical problems
Participate in on-call rotations, incident responses, and postmortems utilizing data-driven insights to identify and remediate reliability risks.
Collaborate with product, architecture, security, and operations teams to embed reliability, security, and AI best practices throughout the software development lifecycle
Identify high-impact opportunities and design end-to-end GenAI solutions including RAG pipelines, vector databases, LLM integrations, and agentic workflows in driving them from concept to production
Deliver AI-powered products with simple user workflows for non-technical stakeholders in converting technical complexity into usable features and business impact
Ensure solutions meet regulatory, compliance, privacy, and model governance requirements for financial services
Provide hands-on coding expertise and mentor engineers who are new to AI to establish reusable patterns, lead workshops, and build the team's ability to independently deliver AI solutions over time
Lead evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
Lead communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies