At Logz.io, we’re redefining observability for the age of AI. Trusted by 1,000+ customers worldwide, our platform helps engineering teams monitor, troubleshoot, and secure complex cloud environments. Now, with OrionIQ, we’re taking the next step toward AI-powered observability that doesn’t just detect problems, but helps teams understand and resolve them.
The Solution Engineer is a highly technical, core member of the sales and customer success organization, aligning our cutting-edge observability and agentic AI technologies with our enterprise customers' architectures. We are customer-obsessed, highly technical, and foster a team culture that is entrepreneurial, professional, and fun.
In this role, you will partner closely with Sales and Account Managers, bringing deep technical expertise to customer engagements, POCs, and solution validation. You will play a key role in demonstrating product value, driving successful adoption, and identifying technical opportunities for upsell and cross-sell, helping expand customer usage and drive revenue growth.
Location: Hybrid (3 days from the office)
Workweek: Monday–Friday
Responsibilities
Architectural Ownership: Manage all technical touchpoints for strategic customers, driving deep-tech onboarding, advanced enablement, architectural workshops, and complex issue resolution.
Value Optimization: Analyze customer data pipelines and infrastructures to develop and execute technical strategies that maximize observability value and ROI.
Technical Stakeholder Management: Build deep technical peer relationships with DevOps, SRE, and Engineering leaders, diagnosing complex infrastructure challenges and designing production-ready solutions.
POCs & Technical Validation: Partner with Sales to execute technical POCs, validate customer use cases, demonstrate product value, and address technical blockers to support purchasing and expansion decisions.
Technical Expansion & Upsell: Identify technical opportunities for upsell and cross-sell by uncovering new use cases, increased usage potential, and customer needs for additional modules, including agentic AI solutions. Partner with Sales to validate these opportunities and demonstrate their technical value.
Advanced Deployment: Ensure a frictionless, deeply integrated onboarding process for enterprise customers, managing complex log/metric/trace routing and agentic AI deployments.
Adoption Analytics: Monitor deep-tech adoption patterns and infrastructure usage, proactively debugging systemic bottlenecks, scaling friction, or implementation barriers.
What Your Day-to-Day Will Look Like
Manage customer technology interactions: Support technical discovery, solution demonstrations, POCs, onboarding, integration, enablement, implementation assistance, and AI use case adoption.
Automating Integration Workflows: Design and implement automated, scalable onboarding pipelines and configuration workflows to guide enterprise environments through seamless setup.
Proactive Architecture Reviews: Conduct deep-dive engineering check-ins around critical milestones (e.g., post-onboarding, scaling peaks, or architecture reviews) to ensure optimal system performance.
Telemetry & Infrastructure Audits: Analyze customer data volume, telemetry pipeline health, and behavioral patterns to catch underperforming configurations or indexing issues before they impact the customer.
Feedback Integration: Gather technical feedback, feature gaps, and product limitations from engineering stakeholders to advocate for and collaborate with internal product and R&D teams.
Technical Upsell Engineering: Analyze data ingestion and infrastructure usage to engineer custom product/feature recommendations tailored to the customer's specific technical scale, with a focus on agentic workflows.
Requirements
Experience: 3+ years of experience as a solution engineer, technical account manager (TAM), or DevOps/SRE engineer with strong customer-facing capabilities.
Domain Expertise: Extensive hands-on technical experience in SaaS infrastructure, cloud architectures (AWS/Azure/GCP), observability solutions (log management, APM, tracing, and OpenTelemetry), and agentic solutions.
Technical Communication: Strong customer-facing engineering skills—expert at training engineering teams, whiteboarding complex cloud topologies, resolving technical roadblocks, and translating deep-tech architectures into clear business value.
Language Skills: Exceptional written and verbal professional communication skills in English.
Mindset: Driven, self-starter technical professional with a passion for problem-solving and a desire to constantly push boundaries in AI and observability technology.
Education: BA/BSc degree in Computer Science, Software Engineering, or equivalent deep technical experience.f you’re excited to make an impact, take ownership, and help shape the future of AI-powered observability, we’d love to meet you.
Skills Required
- 3+ years of experience as a solution engineer, technical account manager, or DevOps/SRE engineer with strong customer-facing capabilities
- Hands-on experience with SaaS infrastructure and cloud architectures, including AWS, Azure, or GCP
- Experience with observability solutions, including log management, APM, tracing, and OpenTelemetry
- Experience with agentic solutions and AI use cases
- Strong customer-facing engineering and technical communication skills
- Ability to train engineering teams, whiteboard complex cloud topologies, and resolve technical roadblocks
- Exceptional written and verbal professional communication skills in English
- Driven, self-starting approach with strong problem-solving skills and interest in AI and observability technology
- BA/BSc degree in Computer Science, Software Engineering, or equivalent deep technical experience
What We Do
Logz.io helps cloud-native businesses monitor and secure their environment with comprehensive open source tools. Our platform turns observability from a high-cost, low-value burden to a high-value, cost-efficient enabler of better business outcomes by combining and extending familiar, powerful and relevant Open Source capabilities across Logs, Metrics and Traces - complemented by security monitoring in the form of cloud-based SIEM.
