The Role
As a Machine Learning Engineer, you will build and scale ML infrastructure, manage deployments, and optimize models for production, collaborating with researchers and engineers.
Summary Generated by Built In
About the Role
Responsibilities
Requirements
Why Join Alinia?
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.
- 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.
- 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.
- 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.
Skills Required
- 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)
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The Company
What We Do
Alinia enables companies to control & audit their AI Agents in a simple and scalable way. We give human experts superpowers to build their own AI QA validators, auditors, and guards through our no-code control platform and our family of proprietary compliance models.
