Senior AI Platform Engineer ( Enterprise Systems)
About this role
Designing applications and services, operating inference platforms and pipelines, and treating unit economics as engineering constraints define the day-to-day routine. Motive maintains a platform empowering physical operations teams across transportation, logistics, and manufacturing by combining software with industry-leading AI to automate workflows and provide fleet visibility. Enterprise systems engineers build production software and AI systems on reliable, secure cloud infrastructure while collaborating closely with product and business stakeholders to drive down unit costs.
Successful contributors possess a strong background in software engineering and cloud infrastructure, balancing feature delivery with strict cost governance. Candidates who thrive in this environment approach GPU spend, idle capacity, and token optimization as core technical challenges rather than purely financial metrics.
What you will do
- Design, build, and deploy production services, APIs, and user-facing interfaces while managing the full software lifecycle.
- Develop and operate production AI systems encompassing retrieval-augmented generation, evaluation, fine-tuning, and inference optimization.
- Architect cloud environments utilizing AWS or GCP alongside Kubernetes, Terraform, and continuous integration pipelines.
- Maintain system observability, manage incident response, and align architectures with compliance frameworks like SOC 2 and SOX.
- Monitor and reduce cloud and AI expenditures by optimizing GPU utilization, routing, model selection, and resource right-sizing.
- Partner with cross-functional product and engineering teams to deliver working software that improves reliability and lowers unit costs.
What you need
- Brings five or more years of professional software development experience owning production systems.
- Demonstrated history of shipping backend services, APIs, and production artificial intelligence or machine learning applications.
- Proficiency in Python paired with a secondary programming stack and frameworks such as PyTorch or TensorFlow.
- Hands-on operational experience with AWS or GCP, Kubernetes, Terraform, and automated deployment pipelines.
- Track record of actively reducing or governing cloud infrastructure and AI operational spend.
- Capable of clearly communicating technical and financial trade-offs while maintaining ownership of deployed services.
Nice to have
- Full-stack development using React and Next.js
- Experience with vector databases and inference optimization
- Familiarity with FinOps tooling and compliance standards like SOX and SOC 2
- Usage of developer productivity tools such as GitHub Copilot and Cursor
Skills and tech stack
Questions candidates ask
What is the remote work policy?
This role is offered as remote within India. Some interviews or new-hire training sessions may occasionally be held in person at one of the global offices.
What technologies will I work with?
Engineering duties involve writing maintainable code in Python alongside a second stack such as TypeScript, Go, or Java. Infrastructure is built on AWS or GCP using Kubernetes, Terraform, and various AI serving frameworks.
What does success look like in this role?
Success involves shipping production software and AI systems with defined SLOs and genuine ownership rather than simple demos. Reliability must remain high while cloud and GPU waste trends downward through visible and improving cost metrics.