Senior Machine Learning Engineer

About the role

The company develops AI‑driven solutions for defense and infrastructure. As a Senior Machine Learning Engineer you will lead the full lifecycle of advanced AI models for an automated requirements engineering platform and an intelligent supply‑chain solution.

Key responsibilities

• Design and implement LLM pipelines to parse complex regulatory texts and extract structured rules.
• Develop Retrieval‑Augmented Generation (RAG) architectures for semantic search across technical documentation.
• Build time‑series forecasting, classification and anomaly‑detection models for demand forecasting and supplier risk assessment.
• Create multi‑objective optimization algorithms for procurement decision‑making.
• Containerize models with Docker and Kubernetes and deploy them in secure on‑premise environments.
• Set up automated training and inference pipelines using Kubeflow or MLflow, and monitor model drift in production.

Required profile

• 5+ years of experience in Machine Learning Engineering with production deployments.
• Proven ability to apply ML techniques to specialized domains such as defense, supply‑chain or construction.
• Experience working in agile, sprint‑based environments and adhering to strict engineering documentation standards.
• Strong communication skills for collaboration with data scientists, backend engineers and domain experts.

Required skills

• Python programming.
• Machine learning libraries: PyTorch, TensorFlow, Scikit‑learn, Pandas, NumPy.
• Transformer models (BERT, GPT, Llama) and NLP frameworks (Hugging Face, LangChain).
• MLOps tools: Docker, Kubernetes, MLflow, Kubeflow.
• Data handling: SQL and tabular data processing, text/PDF parsing.
• Experience with RAG architectures, prompt engineering, time‑series forecasting, classification, anomaly detection, model quantization and distillation.