MLOps Lead Engineer

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within the Consumer & Community Banking Home Lending 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.

Job 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

 

 

Required qualifications, capabilities, and skills

 

• 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.

• 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