Lead Software Engineer - AI, Python/Java

Are you passionate about building innovative technology that powers AI and machine learning across a global organization? As part of our team, you’ll help shape the future of model deployment at scale, collaborating with talented engineers and data scientists. You’ll have the opportunity to work on impactful projects, grow your skills, and contribute to a platform that drives real business outcomes. We value creativity, collaboration, and a commitment to excellence.

As a Lead Software Engineer at JPMorgan Chase within Firmwide AI/ML Deployment Platform team, you will work closely with engineers to design, build, and deploy an AI solution that unifies observability data across multi-cloud environments (AWS, Azure, GCP). You will create intelligent systems that correlate cross-platform health metrics, logs, and traces to generate actionable troubleshooting recommendations and automations, enabling self-service remediation, predicting system outages before they occur, and directly reducing ticket volume by identifying and resolving repeated IT incidents. This role bridges the gap between machine learning, multi-cloud infrastructure, and automated IT operations to build predictive, self-healing solutions.

Job responsibilities

• Build and deploy infrastructure solutions for seamless integration of control plane and user accounts
• Design pipelines to ingest, aggregate, and correlate telemetry data (metrics, logs, traces) from multi-cloud infrastructures.
• Architect and implement closed-loop automation playbooks that allow infrastructure to auto-remediate common, repeatable failure modes without human intervention.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• 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.
• Build and operationalize LLM/machine learning models for anomaly detection, predictive health monitoring, and forecasting system degradations.
• Integrate the AI engine with ticket data and map observability insights against ticket trends, cluster repetitive issues, and quantify the platform’s impact on reducing Mean Time to Resolution (MTTR).
• Create user-friendly self-service portals or conversational AI interfaces that allow non-expert teams to diagnose and fix infrastructure issues safely.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• AI/ML & Data Science: Strong proficiency in Python, Java alongside experience integrating LLM / ML models. Familiarity with time-series forecasting data analysis pipelines and Natural Language Processing (NLP) for log/ticket clustering is essential. Good understanding of agentic AI concepts (A2A, MCPs, Skills, RAG, etc)
• Automation & Orchestration: Advanced experience with configuration management tools and automated workflow engines.
• Integration: Hands-on experience building custom webhooks, APIs, and integrations with ticketing systems like ServiceNow or Jira Service Management.
• Big Data Pipelines: Competency in managing large-scale, streaming data infrastructure using cloud-native data warehouses (e.g. Snowflake)
• Cloud & Infrastructure: Expertise in multi-cloud architectures across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) including on-prem.
• Observability Frameworks: Experience with enterprise observability stacks such as OpenTelemetry, Prometheus,  Dynatrace
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• **Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expecta…