Lead AI Software Engineer for Multi-Cloud & Automation

Next Frontier Capital

Jersey City (NJ)

On-site

USD 150,000 - 210,000

Full time

14 days+

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Job summary

JPMorgan Chase & Co. is seeking a Lead Software Engineer to join the Firmwide AI/ML Deployment Platform team.

You will design, build, and deploy AI solutions that unify observability data across multi-cloud environments (AWS, Azure, GCP), correlating metrics, logs, and traces to enable self-service remediation and predictive health. You will lead development of LLM/ML models, integrate with ticketing systems, and drive adoption of AI-assisted engineering practices while ensuring secure,

Qualifications

Formal training or certification in software engineering concepts and 5+ years applied experience.
AI/ML & Data Science: Strong proficiency in Python, Java; experience integrating LLM/ML models; familiarity with NLP for log/ticket clustering.
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 AWS, Azure, and GCP including on-prem.
Observability Frameworks: Experience with OpenTelemetry, Prometheus, Dynatrace.
Responsible AI: Guidance on safe, compliant adoption, data sensitivity handling, and security.

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, while establishing validation standards and promoting reuse of patterns.
Applies knowledge of tools within the SDLC toolchain to improve the value realized by automation.
Build and operationalize LLM/ML 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 MTTR.
Create user-friendly self-service portals or conversational AI interfaces that allow non-expert teams to diagnose and fix infrastructure issues safely.