MLOps Engineer
Share
Job Location:
Monthly Salary:
Posted on:
16 hours ago
Vacancies:
1 Vacancy
Job Summary
MLOps Engineer:
Responsibilities
- Lead the design and implementation of APIs SDKs and middleware to integrate AI platform services into Keysights software ecosystem.
- Collaborate with product teams across T&M EDA and LLM frameworks to understand integration requirements and deliver tailored solutions.
- Build robust interfaces for model deployment inference and monitoring within existing software stacks.
- Ensure compliance with security performance and usability standards across all integration points.
- Mentor junior engineers and contribute to architectural decisions for scalable AI service delivery.
- Work closely with MLOps and data engineering teams to align infrastructure with product needs.
Qualifications
- Strong programming expertise in Python and C with demonstrated experience building production-grade APIs and microservices.
- Deep understanding of distributed systems and service-oriented architecture including experience with gRPC RESTful APIs and message queues (e.g. Kafka RabbitMQ).
- Hands-on experience with containerization and orchestration including Docker Kubernetes and Helm for scalable deployment across hybrid environments.
- Proficiency in CI/CD pipelines using tools like GitLab CI Jenkins or ArgoCD with a focus on automated testing deployment and rollback strategies.
- Experience integrating ML models into software products including model serving (e.g. TorchServe Triton) inference optimization (e.g. ONNX TensorRT) and runtime monitoring.
- Familiarity with cloud-native development on AWS or Azure including IAM networking and cost optimization strategies.
- Strong debugging and performance profiling skills with experience using tools like Valgrind perf gdb and Prometheus/Grafana for observability.
- Working knowledge of secure software development practices including authentication authorization and data protection in AI workflows. MLOps Engineer:
Responsibilities
- Lead the design and implementation of APIs SDKs and middleware to integrate AI platform services into Keysights software ecosystem.
- Collaborate with product teams across T&M EDA and LLM frameworks to understand integration requirements and deliver tailored solutions.
- Build robust interfaces for model deployment inference and monitoring within existing software stacks.
- Ensure compliance with security performance and usability standards across all integration points.
- Mentor junior engineers and contribute to architectural decisions for scalable AI service delivery.
- Work closely with MLOps and data engineering teams to align infrastructure with product needs.
Qualifications- Strong programming expertise in Python and C with demonstrated experience building production-grade APIs and microservices.
- Deep understanding of distributed systems and service-oriented architecture including experience with gRPC RESTful APIs and message queues (e.g. Kafka RabbitMQ).
- Hands-on experience with containerization and orchestration including Docker Kubernetes and Helm for scalable deployment across hybrid environments.
- Proficiency in CI/CD pipelines using tools like GitLab CI Jenkins or ArgoCD with a focus on automated testing deployment and rollback strategies.
- Experience integrating ML models into software products including model serving (e.g. TorchServe Triton) inference optimization (e.g. ONNX TensorRT) and runtime monitoring.
- Familiarity with cloud-native development on AWS or Azure including IAM networking and cost optimization strategies.
- Strong debugging and performance profiling skills with experience using tools like Valgrind perf gdb and Prometheus/Grafana for observability.
- Working knowledge of secure software development practices including authentication authorization and data protection in AI workflows.
