(Senior) Machine Learning Engineer
Pubblicato il 21-08-2026 - Altro in Italia
We are a fast-growing HR tech startup backed by leading international VCs, having raised €9M+ from 360 Capital, IFF, Kfund, and 14Peaks. We are a team of 30+ professionals passionate about shaping the future of talent assessment. Skillvue is a Skill AI Assessment platform (SaaS) to hire top-skilled candidates and measure employee skill, culture, and leadership at scale to upskill and grow the workforce by leveraging AI. Role overview You will own end-to-end ML systems: model training, fine‐tuning, deployment, monitoring, and cost/performance optimization. Partner closely with organizational psychologists, people scientists, and software engineering to productionize LLMs, real‐time conversational agents, and ML pipelines. You will report to the Head of AI & Science and drive engineering best practices, reliability, and reproducibility across the stack.
Design, build, and maintain end-to-end ML platforms and pipelines: data ingestion, feature engineering, training, validation, deployment, and monitoring.
Develop, fine‐tune,
and deploy LLMs and GenAI services for assessment tasks (prompt engineering, instruction tuning, RLHF/IL, retrieval‐augmented generation).
Build infrastructure‐as‐code (Terraform/CloudFormation) for reproducible environments and secure, compliant deployments.
Create automated CI/CD for data, models, and infra (model/data versioning, reproducible training runs, canary/blue‐green deployments).
Implement monitoring, observability, drift detection, and alerting for model performance and data pipeline health; Integrate vector databases, retrieval pipelines, and caching strategies for RAG systems; Ensure data and model governance: lineage, access controls, privacy safeguards, and auditability.
Bachelor or Master's degree in Computer Science or related field.
~7+ years experience in ML engineering/ML‐Ops delivering production ML products.
~3+ years practical experience training and deploying GenAI/LLMs in production.
~ Strong production experience on AWS