MLOps Engineer

We are building a strong DevOps, MLOps, and DataOps team to test, validate, and deploy production-grade Computer Vision and AI pipelines. The team's mission is to handle pipelines running on a wide range of hardware, from milliwatt edge devices like Jetson Orin and Hailo-8/15 to multi-GPU data-center nodes. As a member of this team, you will need to master DevOps, MLOps, and DataOps while maintaining hardware awareness. This is a senior-level position, and you will report to the CTO.

Purpose of the Role

As a junior member of the team, you will support the process of moving AI/ML artifacts from R&D to production. Your key contributions will involve:

* Assisting with functional testing, including unit, integration, and regression tests.

* Helping to validate models against key metrics like latency, throughput, and accuracy. Supporting the path to production through tasks related to containerization, CI/CD, and monitoring.

Role Focus and Time Allocation

Focus: Your work will be centered on edge testing, edge deployment, automated regression, and release operations.

Time Allocation:

•Testing: 60%

•Deployment & Ops: 40%

*Responsibilities*

In this entry-level role, you will learn and apply the following, with guidance from senior team members:

*Build & Packaging:*

Edge: Assist with tools like TensorRT, ONNX, and Hailo NN-Converter.

Server / Data-Center: Work with Yocto / Ubuntu Core images.

Cross-Cutting: Use Docker/OCI containers, help package testing environments, and work with Git-versioned artifacts.

Functional & Model Validation:

•Edge: Support model quantization and calibration tasks.

•Server / Data-Center: Help run fairness tests.

•Cross-Cutting: Work with regression datasets in DVC.

Performance & Power Testing:

•Edge: Use tools for Jetson power-profiling and assist in thermal testing.

•Server / Data-Center: Support bandwidth and saturation tests.

•Cross-Cutting: Help measure and report latency and throughput.

Preferred Skills

Skills

We are looking for candidates with a foundational skill set who are eager to learn. Basic familiarity with performance testing and Docker/Kubernetes deployments is required.

Modern C++ (17/20/23):

•Basic CMake skills and familiarity with gdb.

AI Runtimes – C++ APIs:

•ONNX Runtime (C++): Ability to run sample applications.

•TensorRT (C++): Experience with trtexec.

•NVIDIA Triton: Capable of deploying a prebuilt model.

•DeepStream (C/C++): Ability to run the reference application.

Edge & Embedded Tool-chain:

•Jetson (L4T): Proficient with ssh and tegrastats.

•Hailo-8/15: Able to run samples.

•Intel ARC / OpenVINO: Can run basic hello_query_device.

Build & Packaging (C++):

•CMake: Can build from a given README file.

•Conan / vcpkg: Able to install dependencies.

•Cross-compilation: Can use prebuilt images.

Performance & Profiling:

•CPU: Familiar with htop.

•GPU: Familiar with nvidia-smi.

Testing C++ Pipelines:

•Unit / Integration: Can write simple assert statements for testing.

•Regression & KPI: Able to run scripts, collate data and KPIs, and report them to the R&D team.

DataOps & Telemetry:

•gRPC / ZeroMQ C++: Can run example code.

•Logging & Tracing: Can use printf for logging.

Experience

• 3 -5 years of experience.
• Solid C++ coursework or open-source contributions (please provide proof via GitHub links).

Job Types: Full-time, Permanent

Education:

• Bachelor's (Preferred)

Experience:

• total work: 5 years (Preferred)
• C++: 5 years (Preferred)

License/Certification:

• C++, Rust, Java, or Kotlin (Preferred)

Work Location: In person