Website:
wraithint.com
Job details:
Lead Systems Engineer - Build the systems that make autonomy work.
At Wraith Int Tech Solutions, we are building intelligent systems that bring together AI, perception, embedded computing, control, sensors, and actuation—and make them work reliably outside the lab.
We are looking for a Lead Systems Engineer to take ownership of this integration.
This is not a role where you will simply manage requirements or coordinate engineering teams. You will be hands-on in the architecture, development, integration, debugging, and field validation of complex autonomous and semi-autonomous systems.
You will work across software and hardware, make engineering trade-offs, solve problems that do not fit neatly into one discipline, and lead a team toward building systems that perform reliably under real-world constraints.
If you enjoy understanding how an entire system works—not just one component—and want to see your engineering decisions translate into a working fielded product, this role is for you.
What You Will Own
1. Systems Architecture & Integration
Architect end-to-end systems combining AI/perception, embedded software, control systems, sensors, communication interfaces, and actuators.
Define system architecture, subsystem boundaries, interfaces, data flows, and integration standards.
Translate system-level requirements into clear technical requirements for individual subsystems.
Identify architectural risks and dependencies early and drive their resolution.
Make pragmatic trade-offs between performance, reliability, compute, power, cost, complexity, and development timelines.
Own system-level technical decisions from early architecture through integration and deployment.
2. Hands-on Software Engineering
Design and develop production-grade software primarily in C++ and Python.
Build software across critical system layers, including perception/AI interfaces, hardware abstraction, control, communication, sensor processing, and sensor–actuator integration.
Write efficient, maintainable software designed for real-time and resource-constrained environments.
Participate actively in code reviews, architectural reviews, debugging, and technical design discussions.
Establish and enforce engineering practices that improve code quality, maintainability, and reliability.
3. System Integration & Debugging
Own integration across hardware and software subsystems and ensure that individual components work as a cohesive system.
Diagnose failures that cross traditional boundaries—software, firmware, hardware, sensors, networks, and system behaviour.
Perform structured root-cause analysis rather than treating symptoms.
Use logs, telemetry, traces, test data, hardware measurements, and field observations to isolate failures.
Drive corrective actions and introduce safeguards to prevent recurring issues.
4. Edge & Embedded Deployment
Deploy AI and software systems on edge computing platforms operating under real-world compute, memory, power, thermal, and connectivity constraints.
Optimize software for latency, throughput, memory utilization, and deterministic behaviour.
Work with embedded Linux, real-time systems, hardware interfaces, and device-level constraints.
Diagnose deployment issues across different hardware configurations and system states.
Contribute to robust update, recovery, monitoring, and field-deployment strategies.
5. Testing, Validation & Field Readiness
Define the system-level validation strategy from development through field deployment.
Establish test coverage across unit, integration, hardware-in-the-loop, simulation, and system-level testing.
Design tests around real-world operating conditions, failure modes, edge cases, and degraded environments.
Lead system bring-up, integration testing, troubleshooting, and field trials.
Use field data and telemetry to identify weaknesses and feed learnings back into engineering.
Drive systems toward measurable standards of reliability, performance, and operational readiness.
6. Technical Leadership
Lead and mentor engineers across software, embedded, AI, hardware, and systems disciplines.
Set technical direction while remaining close enough to the engineering to understand and resolve difficult problems.
Break complex system objectives into clear technical workstreams and measurable milestones.
Conduct technical reviews and challenge assumptions where necessary.
Identify blockers, manage technical dependencies, and keep multiple engineering streams moving toward a common objective.
Build a culture of ownership, engineering rigour, experimentation, and accountability.
7. Cross-Functional Ownership
Work closely with AI, software, embedded, electronics, mechanical, testing, and product teams.
Translate system-level objectives into actionable engineering requirements.
Coordinate integration activities across disciplines and ensure interfaces are understood and validated.
Communicate technical risks, trade-offs, progress, and decisions clearly to both technical and non-technical stakeholders.
Take ownership of problems that cross organisational or disciplinary boundaries.
What We Are Looking
ForRequired
Bachelor's degree in Computer Engineering, Electrical/Electronics Engineering, Mechatronics, Robotics, Computer Science, or a related engineering discipline.
4–8 years of relevant engineering experience, with significant exposure to system integration and production-grade engineering.
Strong hands-on programming ability in C++ and Python.
Experience integrating multiple technical subsystems into a functioning product or platform.
Strong understanding of software–hardware interfaces, system architecture, debugging, and integration.
Experience developing or integrating systems involving two or more of:
AI / computer vision / perception
Embedded systems
Sensors and sensor interfaces
Control systems
Communication protocols
Actuators / robotics
Edge computing
Demonstrated ability to diagnose complex problems across multiple layers of a system.
Experience with system-level testing, validation, and failure analysis.
Experience deploying software on embedded or edge computing platforms.
Strong technical communication and the ability to lead engineers toward a common technical objective.
Preferred
Master's degree in a relevant engineering discipline.
Experience building autonomous or semi-autonomous systems.
Experience in robotics, unmanned systems, drones, autonomous vehicles, industrial automation, defence technology, or other mission-critical systems.
Strong understanding of real-time systems, embedded Linux, RTOS, or hardware-in-the-loop testing.
Experience with system bring-up and field trials.
Experience working with GPU/AI accelerators or edge AI platforms.
Familiarity with sensor fusion, perception pipelines, control architectures, or distributed systems.
Experience leading a small engineering team or owning a major technical workstream.
Exposure to safety-critical, high-reliability, or production-grade engineering environments.
The Kind of Engineer Who Will Thrive Here
You are likely to do well in this role if you:
Think in systems, not silos.
Enjoy getting your hands dirty with code, hardware, logs, and test equipment.
Can move from a high-level architecture diagram to debugging an issue at the subsystem level.
Are comfortable making decisions with incomplete information.
Prefer solving the root cause over finding a temporary workaround.
Can challenge an architecture when it isn't going to work in the real world.
Enjoy working across disciplines rather than staying within a single technical boundary.
Can lead engineers through technical problems without losing the ability to engineer yourself.
Care about whether a system actually works in the field, not just whether it works in a demonstration.
What Success Looks Like
Success in this role is not measured by how many meetings you attend or documents you produce.
It looks like:
A system that works.
A system where the hardware and software operate as one, where failures can be diagnosed systematically, where performance is measurable, and where the engineering team can repeatedly take a complex idea from architecture → integration → validation → field deployment.
You will be successful when:
Critical subsystems integrate predictably.
System-level failures are identified and resolved at their root.
Performance and reliability improve with every development cycle.
Field issues are converted into engineering improvements.
Engineers around you become stronger technically.
And the systems your team builds can perform reliably when real-world conditions are no longer forgiving.
Why Wraith?
At Wraith, you will not be joining a large organisation where your contribution disappears into a single subsystem.
You will have the opportunity to own meaningful parts of the system, work directly with senior leadership and engineering teams, and see your decisions translate into real products and field capability.
We are building at the intersection of AI, robotics, embedded systems, computer vision, and intelligent machines—and we are looking for engineers who want to solve difficult problems that exist beyond the boundaries of a single discipline.
If you want to build systems, not just software, we want to hear from you.
Industry
Defense and Space Manufacturing
Employment Type
Full-time
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