Senior Machine Learning Engineer

Location: Montà

We are a fast-growing HR tech startup backed by leading international VCs, having raised €9M+ from 360 Capital, IFF, Kfund, and 14

Peaks. 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. In today's evolving labour market, skills have become the top priority for HR departments and their importance will only continue to grow.

Our platform enables companies to conduct Skill AI Assessments for both external and internal hiring, as well as targeted evaluations across their entire workforce.
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 product ionize 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.
Key responsibilities   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).
Implement scalable, low‑latency inference systems (serverless and/or containerized), real‑time voice/text conversational agents, and batching strategies for cost‑effective throughput.
Build infrastructure‑as‑code (Terraform/Cloud Formation) 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).
Optimize model size and inference cost using quantization, pruning, distillation, sharding, and hardware‐aware optimizations.
Implement monitoring, observability, drift detection, and alerting for model performance and data pipeline health; run A/B and multivariate experiments to validate model changes.
Integrate vector databases, retrieval pipelines, and caching strategies for RAG systems; manage embeddings lifecycle and similarity search performance.
Ensure data and model governance: lineage, access controls, privacy safeguards, and auditability.
Required qualifications   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 (Sage Maker, Lambda, ECS/EKS, Bedrock experience is a plus).
Proven track record building highly scalable services and real‑time systems.

Experience with infrastructure‑as‑code (Terraform, Cloud Formation) and container orchestration (Docker, Kubernetes).
Hands‑on experience with ML pipeline and experiment platforms (MLflow, Weights & Biases, Kubeflow, Airflow/Prefect).
Proficiency in Python and Type Script; solid software engineering practices and Git workflows.
Experience implementing model monitoring, drift detection, and A/B testing for ML models.
Familiarity with vector DBs (e.g., Qdrant), retrieval pipelines, and prompt/agent design.
Fluency in English (C1) and strong communication for cross‑functional collaboration.
Highly desirable   

Experience with Bedrock, Sage Maker, or other managed LLM infrastructures.

Experience of deploying in multimodal models, speech‑to‑text, text‑to‑speech, and building voice‑based conversational agents.

Experience with distributed…