Sr. Forward Deployed Engineer

About Us

Logic Monitor is the AI-first hybrid observability platform powering the next generation of digital infrastructure.

We love going to work and think you should too.

Our team is dedicated to trust, customer obsession, agility, and striving to be better everyday.

These values serve as the foundation of our culture, guiding our actions and driving us towards excellence.

We foster a culture of performance and recognition, allowing us to transform growth as we enable our employees to do the best work of their careers.

Location: London, England. Our office is situated in a core location near Waterloo and Blackfriars on the Southbank.

What You'll Do

Logic Monitor delivers complete visibility and actionable intelligence across on-premises, cloud, and edge environments.

By anticipating issues before they strike, optimizing resources in real time, and enabling faster, smarter decisions, Logic Monitor helps IT and business leaders protect margins, accelerate innovation, and deliver exceptional digital experiences without compromise.

We’re looking for a Sr.

Forward Deployed Engineer who enjoys solving real customer problems through hands-on engineering and AI-driven solutions.

In this role, you will work closely with senior engineers, product teams, and customers to design, build, and deploy tailored solutions that extend Logic Monitor Edwin AI.

You will be operating at the intersection of customer delivery and core platform development, owning discovery, scoping, system design, build, rollout, and measurable adoption.

This is a customer‑facing engineering role at the intersection of software engineering, observability, and AI deployment.

You will ship code, implement integrations, support production rollouts, and codify repeatable patterns that improve both customer outcomes and the core platform.

• Build & Support AI-Powered Solutions
• 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 Java Script or Type Script.
• Implement REST API, webhook, and SDK based integrations that normalize, enrich, and route operational data into Logic Monitor Edwin AI.
• Instrument and troubleshoot telemetry across logs, metrics, traces, and related workflow data, including Open Telemetry based pipelines where appropriate.
• Configure and deploy solutions in Saa S, hybrid cloud, and customer‑managed environments, with working familiarity across containers, Kubernetes, CI/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, hyper‑care, 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.
• Technical Execution
• Write clean, maintainable code across backend and frontend systems, commonly using Python, Java Script, Type Script, 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.
• Reusable artifacts created, such as templates, integration patterns, playbooks, or productized improvements.
• Prototype & Iterate
• 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.
• Customer & Partner Collaboration
• 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.
• Production deployments launched and adopted by named customer teams.
• Time to first value, such as first workflow live, first integration live, or first meaningful AI investigation or automation in production.
• Measurable workflow impact, such as reduced manual triage, faster incident handling, lower alert noise, or improved operational efficiency.
• Strong implementation quality, documentation quality, and stakeholder satisfaction.
• What You'll Need
• 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 Java Script or Type Script, 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 Open Telemetry 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/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 trade‑offs to engineers, product teams, and customer stakeholders.
• Nice to have: Service Now or Jira workflows, automation tooling such as Ansible, Terraform, or Rundeck, and familiarity with MCP or similar integration patterns.
• Equal Opportunity Employer

Logic Monitor is an Equal Opportunity Employer.

At Logic Monitor, we believe that innovation thrives when every voice is heard and each individual is empowered to bring their unique perspective.

We’re committed to creating a workplace where diversity is celebrated, and all employees feel inspired and supported to contribute their best.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

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