About the Role
As Alinia’s Machine Learning Engineer, you will be responsible for building and scaling the ML infrastructure that powers our AI guardrails, evaluation pipelines, and enterprise-grade deployments. You’ll work at the intersection of cutting-edge ML research and production, ensuring that our models and detectors move seamlessly from prototype to reliable, performant, and secure systems used in real-world regulated environments.
This is a hands-on, high-impact role where you will own the full lifecycle of ML engineering: from infrastructure design, deployment pipelines, and monitoring, to optimization and productionization of research outputs.
Responsibilities
- Build and maintain robust ML infra (training, serving, monitoring).
- Deploy LLMs, RAG pipelines, and detectors into production at scale.
- Translate research prototypes into production-ready APIs/services.
- Manage CI/CD pipelines, observability, experiment tracking.
- Optimize for latency, cost, and reliability.
- Ensure security, compliance, and privacy in enterprise environments.
- Collaborate closely with ML researchers, back-end engineers, and product teams.
Requirements
- 4+ years as ML Engineer / MLOps / related role.
- Strong Python + ML frameworks (PyTorch/TensorFlow).
- Cloud platforms (AWS/GCP/Azure) + Kubernetes/Docker.
- Track record deploying ML models in production (REST/gRPC, FastAPI).
- CI/CD pipelines, monitoring, experiment tracking (MLflow, W&B).
- Understanding of enterprise security & compliance (SOC2, ISO 42001, EU AI Act).
Nice to have:
- LLMs, RAG systems, or retrieval optimization.
- Experience with GPUs/distributed training.
- Work in regulated industries (finance, insurance, healthcare).
- Contributions to open-source ML tooling.
Why Join Alinia?
- Build AI safety infrastructure at the frontier of enterprise adoption.
- Work on applied ML with real-world impact.
- Competitive compensation + meaningful equity.
- Early, high-impact role in a mission-driven startup.
