As a Lead Software Engineer at JPMorgan Chase withinFirmwide 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 expectations; experience coaching engineers on safe, compliant adoption within delivery practices

Preferred qualifications, capabilities, and skills

• Practical experience applying generative AI and agentic workflows to accelerate development (e.g., AI-assisted code and test generation, refactoring, documentation), with strong judgment, governance, and quality control over AI-produced outputs.
• Experience optimizing performance and reliability of AI-powered user interfaces & proficiency in React framework
• Knowledge of multi clouds domains
• Experience working with LLMs
• Experience in API development and design