Senior Data Scientist & ML Engineer

About the role

Foodics is looking for a senior‑level professional to lead the design, development and deployment of machine‑learning and generative‑AI models that power its core restaurant‑management products such as pricing, personalization and fraud detection. The role sits at the intersection of data science, engineering and product, working closely with data engineers, product managers and platform teams to deliver production‑grade models with measurable business impact.

Key responsibilities

• Own the full ML model lifecycle—from problem framing and data exploration to training, deployment, monitoring and continuous improvement.
• Design and implement scalable solutions using classical machine‑learning algorithms and generative‑AI techniques.
• Apply MLOps best practices, including versioning, reproducibility, CI/CD pipelines and model observability (MLflow, SageMaker, etc.).
• Collaborate with cross‑functional squads to ensure model reusability, compliance with standards and seamless integration with APIs and backend services.
• Mentor junior ML engineers and contribute to the internal knowledge base.
• Adopt a “you build it, you run it” mindset, taking responsibility for model performance, drift detection and fairness.

Required profile

• 5+ years of hands‑on experience in applied machine learning, AI or data science.
• Proven track record of deploying ML models at scale in production environments.
• Strong ability to communicate technical concepts to both technical and non‑technical audiences.

Required skills

• Python programming.
• ML/AI libraries: scikit‑learn, PyTorch, TensorFlow, XGBoost, HuggingFace Transformers.
• MLOps tools: MLflow, SageMaker, CI/CD, GitOps.
• Cloud platforms (AWS preferred) and infrastructure‑as‑code tools such as Terraform or CDK.
• GenAI/LLM integration: RAG, fine‑tuning, embeddings, prompt engineering, LangChain, LangGraph, LlamaIndex.
• Statistical modeling, feature engineering, hyper‑parameter tuning, A/B testing, bias mitigation and model explainability (SHAP, LIME).