Lead Principal Field Systems Engineer

Description

We are looking for senior Field Systems Engineers who can go beyond identifying operational problems to understanding the engineering, data, and technology systems behind them. This role combines hands-on field engineering, quantitative analysis, process engineering, and technical product improvement to help OCI deliver GPU and cloud capacity more predictably at global scale.

Field Systems Engineers operate at the intersection of physical infrastructure, deployment processes, enterprise data systems, and engineering technology. They investigate systemic failure modes, perform engineering studies, develop predictive models, and translate field requirements into improvements across technical products, tooling, data pipelines, and backend architecture.

This is a senior individual contributor role with broad technical ownership and organizational impact. The ideal candidate is comfortable going deep into technical problems, challenging assumptions with data, influencing engineering decisions, and turning complex field challenges into durable solutions that scale across OCI's global infrastructure footprint.

Direct GPU or hyperscale cloud experience is valuable but not mandatory. What matters most is demonstrated engineering rigor, systems thinking, technical depth, and the ability to turn ambiguous problems into measurable improvements.

Responsibilities

What You'll Do

• Partner with Delivery as a hands-on engineering counterpart to improve deployment readiness, commissioning, and production handoff while Delivery retains end-to-end execution ownership.
• Lead technical investigations into recurring deployment issues across site readiness, hardware, networking, cabling, power, cooling, materials, construction, commissioning, and operational handoff.
• Conduct quantitative engineering analyses, including statistical analysis, regression modeling, process capability studies, cycle-time analysis, bottleneck identification, and root-cause investigations.
• Build analytical and predictive models to identify the leading indicators, technical dependencies, and process variables that influence deployment performance, schedule risk, and capacity readiness.
• Evaluate the accuracy, consistency, completeness, and timeliness of operational data used to measure deployment health and support critical infrastructure decisions.
• Partner with Product, Software Engineering, Data Engineering, and Architecture teams to define technical requirements and drive improvements to enterprise tools, application workflows, data pipelines, integrations, and backend systems.
• Help establish reliable, standardized signals of deployment readiness, risk, blockers, commissioning progress, and production availability that can be distributed consistently across OCI's global organization.
• Translate field observations and engineering analysis into clear product requirements, data contracts, validation rules, technical specifications, and process changes.
• Develop readiness gates, go/no-go criteria, validation mechanisms, and technical handoff standards informed by actual system behavior and measurable performance.
• Drive systemic corrective actions with functional owners, ensuring field learnings result in lasting improvements rather than repeated local workarounds.
• Evaluate the effectiveness of engineering, process, and technology changes through measurable results and continued field validation.
• Communicate technical findings, quantified business impact, and recommended actions to senior engineering, operational, and executive stakeholders.

Core Responsibilities

• Apply systems engineering principles to problems spanning physical infrastructure, engineering processes, software systems, data architecture, and organizational dependencies.
• Design and conduct structured engineering studies to test hypotheses, isolate critical variables, quantify failure modes, and evaluate potential solutions.
• Use statistical methods, regression analysis, and other analytical approaches to explain performance variability, identify leading indicators, and improve deployment predictability.
• Diagnose data-quality issues, conflicting system records, missing signals, and gaps between reported status and actual field conditions.
• Understand how operational information flows through source systems, integrations, transformation layers, reporting platforms, and downstream decision processes.
• Work with engineering and product owners to improve technical architecture, instrumentation, event capture, data validation, and system interoperability.
• Translate complex technical issues into concise, decision-ready recommendations supported by evidence and clear tradeoffs.
• Influence engineering priorities and cross-functional execution without relying on direct reporting authority.
• Balance rapid field-level resolution with longer-term engineering and architectural improvements.
• Operate independently in high-pressure environments with incomplete information, evolving requirements, and competing stakeholder priorities.
• Travel approximately 25–33% based on deployment needs and business priorities.

What Success Looks Like

• Deployment risks are identified earlier through reliable technical signals, quantitative analysis, and stronger readiness criteria.
• Engineering studies and analytical models identify the variables driving delays, rework, commissioning failures, and readiness variability.
• OCI has more consistent, trustworthy deployment data flowing through enterprise systems to operational teams and executive decision-makers.
• Product and Engineering teams receive well-defined technical requirements grounded in field evidence and measurable business impact.
• Recurring field problems result in durable improvements to engineering processes, technical products, data architecture, and operational mechanisms.
• Deployment readiness and capacity reporting increasingly reflect actual physical and technical conditions rather than manually reconciled status.
• Improvements reduce repeat blockers, avoidable delays, rework, and manual reporting effort across multiple metros and regions.
• The engineering mechanisms developed continue to generate value beyond a single deployment, escalation, or customer commitment.

Must-Have Qualifications

• 7+ years of experience in systems engineering, process engineering, industrial engineering, field engineering, infrastructure engineering, technical operations, engineering transformation, or similarly complex technical environments.
• Demonstrated experience applying quantitative engineering analysis to operational or technical problems, including statistical methods, regression analysis, process studies, or comparable analytical modeling techniques.
• Experience designing and executing structured root-cause investigations, engineering studies, process optimization initiatives, or technical performance assessments.
• Proven ability to translate analysis into implemented engineering, process, or technology improvements with measurable results.
• Experience working with complex operational datasets and analytical tools such as SQL, Python, R, statistical analysis packages, advanced Excel, or comparable technologies.
• Working understanding of enterprise software systems, data flows, interfaces, system integrations, and the technical dependencies affecting data accuracy and availability.
• Experience partnering with Product, Software Engineering, Data Engineering, or Architecture teams to define requirements and improve technical systems, applications, or data-driven workflows.
• Strong systems-thinking capability across physical infrastructure, operational processes, software, data, and cross-functional dependencies.
• Demonstrated ability to independently investigate ambiguous technical problems, establish ground truth, and drive corrective action.

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