We are looking for a Senior Software Engineer to help build the backend and platform foundation for AI-assisted engineering automation. This role is ideal for someone who enjoys building reliable backend services, long-running workflow execution, data and artifact handling, deployment, observability, and integration with AI/ML services. The engineer in this role will help turn advanced prototypes into reliable internal platforms used by engineering teams. It is also a strong fit for platform engineers who want to grow into AI infrastructure, LLM integration, and agent-enabled engineering workflows.
WHAT YOU WILL DO:
- Build backend APIs and services for engineering automation workflows.
- Design and improve long-running job execution using worker queues such as Celery/Redis or similar
systems.
- Develop reliable handling of engineering data, input files, configurations, logs, intermediate outputs,
final results, and versioned artifacts.
- Improve platform reliability through testing, structured logging, monitoring, error handling, and
failure recovery.
- Containerize, deploy, and operate services using Docker and Linux-based environments.
- Support concurrent users and workloads through scalable service design, queue management, and
resource-aware execution.
- Integrate platform services with automation engines, AI assistants, local models, and external AI/ML
services.
- Work closely with algorithm, AI, and domain engineers to convert engineering workflows into
maintainable software.
Requirements:
5-7 years of hands-on software engineering experience.
- Strong Python backend development.
- Experience with FastAPI, Flask, Django, or similar backend frameworks.
- Experience with background jobs, queues, workers, async processing, or workflow orchestration.
- Hands-on exposure to Redis, Celery, message brokers, caching, or equivalent systems.
- Docker and Linux deployment experience.
- Strong debugging, testing, data modeling, and code review skills.
- Ability to take ownership of ambiguous platform and product problems.
- Comfortable using AI coding assistants while critically reviewing generated code.
GOOD TO HAVE:
- Experience building internal platforms, workflow tools, automation systems, or data-heavy
applications.
- Object storage, artifact management, large-file handling, or result-versioning experience.
- Observability tools such as Prometheus, Grafana, OpenTelemetry, ELK, Loki, or similar.
- Kubernetes or container orchestration exposure.
- Interest or exposure to LLM engineering, AI agents, model-serving APIs, vector databases, GPU-backed
workloads, or MLOps/LLMOps.
- TypeScript/frontend experience for engineering dashboards or visualization-heavy applications.
- Exposure to semiconductor test, validation, CAD/CAE, simulation, manufacturing, or engineering
automation domains.
