DevOps / ML-Ops Engineer

DeepSig Inc•Arlington, VA

9h•Hybrid

About The Position

DeepSig is defining the future of wireless communications by merging deep learning with the Radio Access Network (RAN). We are seeking an experienced DevOps Engineer to work with engineering and QA teams to automate and optimize software and ML model development and validation, supporting existing and upcoming product lines, web applications, and strategic initiatives in 5G and “FutureG” programs. In this role, you will be a key contributor to expanding DevOps capabilities as our organization grows. Your responsibilities will include research, design, development, procurement, provisioning, maintenance, and optimization of DevOps systems leveraging a mix of on-premise and cloud infrastructure.

Requirements

Candidates must be authorized to work in the United States by US citizenship to meet certain information processing and contract requirements
Hands-on experience developing, deploying and ongoing management of source code repositories, CI/CD infrastructure, artifact and package storage, and compute infrastructure with tools such as: AWS, GitLab, MinIO, Nexus, Conan, PyPI, Docker, Slurm, MLFlow, Optuna, Prometheus, Grafana, Terraform
Experience with modern Ubuntu/Debian Linux, containerization services, virtualization technologies, backup solutions, and automation using scripting languages such as: BASH, Python
Experience in networking and dealing with firewall, VLAN and network configuration tools
A demonstrated ability in provisioning hardware and information systems and helping do system engineering to help define new hardware and computing system requirements

Nice To Haves

Experience achieving and maintaining compliance with cyber security standards such as SSDF.
Hand-on experience optimizing CI jobs and infrastructure, especially GitLab.
Experience with build systems, especially CMake.
Experience in or a strong enthusiasm for AI/ML, high-performance computing, SIGINT, SDR, FPGA, or wireless technology.

Responsibilities

Architect infrastructure to support the needs of internal engineering/QA teams, as well as commercial and DoD partners.
Develop a plan to extend infrastructure both on-premise and in the cloud. Includes CI/CD, data storage, compute resources (CPU/GPU), and job orchestration.
Manage the full lifecycle of production systems, from initial system development and deployment to ongoing operation and maintenance ensuring availability requirements.
Specify, procure, and provision hardware as necessary.
Optimize the utilization of CI/CD and compute infrastructure in collaboration with engineering and QA teams.
Identify and address performance bottlenecks.