Machine Learning Engineer

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.