Sr. Forward Deployed Engineer

LogicMonitor•San Francisco, CA

4d•$130,900 - $175,000•Hybrid

About The Position

LogicMonitor is seeking a Sr. Forward Deployed Engineer who is passionate about solving real customer problems through hands-on engineering and AI-driven solutions. This role operates at the intersection of customer delivery and core platform development, with ownership of discovery, scoping, system design, build, rollout, and measurable adoption. The engineer will work closely with senior engineers, product teams, and customers to design, build, and deploy tailored solutions that extend LogicMonitor Edwin AI. This is a customer-facing engineering role that involves shipping code, implementing integrations, supporting production rollouts, and codifying repeatable patterns to improve both customer outcomes and the core platform.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, or a related field.
4-7 years of experience in backend or data systems engineering.
Experience building streaming data pipelines (Kafka / Spark or any similar technology).
Strong programming background in Java and Python, including microservice design.
Experience with ETL, data modeling, and distributed storage systems.
Demonstrated ability to ship clean, production quality code in Java or Python plus JavaScript or TypeScript, whether gained in software engineering, platform engineering, solutions engineering, SRE, or technical consulting.
Demonstrated ability to integrate external systems using APIs, SDKs, authentication controls, payload mapping, and schema validation.
Working knowledge of observability fundamentals, including logs, metrics, traces, dashboards, alerting, root cause investigation, and incident workflows.
Familiarity with OpenTelemetry concepts and telemetry pipelines, including how data is instrumented, collected, and routed across systems.
Working knowledge of modern cloud and deployment tooling such as AWS, Azure, or GCP, containers, Kubernetes, CI or CD, and infrastructure automation tools.
Practical exposure to AI application patterns such as LLM APIs, agents or workflows, evaluation basics, and the observability of AI systems in production.
Strong debugging judgment, systems thinking, and the ability to make progress in ambiguous customer environments.
Awareness of data governance, validation, and lineage best practices.
Strong communication and collaboration across AI, Data, and Platform teams.
Clear written and verbal communication skills, with the ability to explain technical tradeoffs to engineers, product teams, and customer stakeholders.

Nice To Haves

ServiceNow or Jira workflows
Automation tooling such as Ansible, Terraform, or Rundeck
Familiarity with MCP or similar integration patterns

Responsibilities

Partner directly with strategic customers to build production applications, ship technical artifacts used in production workflows, codify repeatable deployment patterns, and represent the company in highly ambiguous enterprise environments.
Partner with Engineers and customers to understand use cases, workflows, and technical requirements.
Build and maintain backend services, integrations, automations, and lightweight full stack experiences using Python and JavaScript or TypeScript.
Implement REST API, webhook, and SDK based integrations that normalize, enrich, and route operational data into LogicMonitor Edwin AI.
Instrument and troubleshoot telemetry across logs, metrics, traces, and related workflow data, including OpenTelemetry based pipelines where appropriate.
Configure and deploy solutions in SaaS, hybrid cloud, and customer managed environments, with working familiarity across containers, Kubernetes, CI or CD, and infrastructure automation.
Define or refine dashboards, alerting, service boundaries, and reliability workflows so customer teams can operate what is deployed.
Partner during go live, hypercare, and troubleshooting periods to drive adoption, reduce friction, and make the solution durable in production.
Convert repeated field learnings into templates, playbooks, reusable components, and product feedback for core engineering and product teams.
Write clean, maintainable code across backend and frontend systems, commonly using Python, JavaScript, TypeScript, and React.
Implement integrations with third-party systems using APIs and SDKs.
Support testing, debugging, and optimization of deployed solutions.
Follow engineering best practices for reliability, security, and performance.
Contribute to internal documentation and knowledge sharing.
Create reusable artifacts such as templates, integration patterns, playbooks, or productized improvements.
Help develop MVPs and proof-of-concept solutions to validate customer use cases.
Incorporate feedback from customers and internal stakeholders to refine functionality.
Support the transition of validated solutions into scalable product capabilities in collaboration with product and core engineering teams.
Participate in technical discussions with customers and partners alongside senior engineers.
Provide implementation support and troubleshooting during deployments.
Share insights and feedback from customer engagements with internal teams to improve product quality and usability.
Ensure production deployments are launched and adopted by named customer teams.
Achieve time to first value, such as first workflow live, first integration live, or first meaningful AI investigation or automation in production.
Deliver measurable workflow impact, such as reduced manual triage, faster incident handling, lower alert noise, or improved operational efficiency.
Ensure strong implementation quality, documentation quality, and stakeholder satisfaction.