Machine learning engineer

At Smart Consulting, you will join innovative projects where data and artificial intelligence are the engine of decision-making. We are looking for a Senior Machine Learning Engineer with strong experience in Python and a focus on building Machine Learning systems in production.

👉 This role is primarily focused on MLOps, ML Platform, and infrastructure, with responsibility across the entire lifecycle of models — from data pipelines to deployment and monitoring.

👉 The focus will not be on researching or developing GenAI models from scratch, but rather on operationalization, scalability, and integration of models in production environments.

If you enjoy building robust end-to-end ML systems — you will feel at home.

What your day-to-day will look like

• Develop and maintain end-to-end data and Machine Learning pipelines
• Build services and APIs for model deployment
• Work on operationalizing models (deployment, scaling, monitoring)
• Implement best practices for MLOps (CI/CD for ML, versioning, tracking)
• Ensure code, data, and pipeline quality (testing, validation)
• Collaborate with Data Scientists to take models from experimentation to production
• Monitor models in production (performance, drift, failures)
• Troubleshoot production issues and optimize existing systems
• Participate in architectural decisions and technology choices
• Mentor other team members

What we are looking for

• +5 years of experience as a Backend Engineer and/or Machine Learning Engineer
• Solid experience in Python (production)
• Experience building end-to-end ML pipelines
• Experience developing APIs to serve models
• Strong knowledge of model lifecycle (training → deployment → monitoring)
• Experience with SQL and/or NoSQL
• Experience with MLOps practices
• Experience with cloud (preferably AWS)
• Experience with Docker and Kubernetes
• Ability to make technical decisions and lead initiatives
• Fluent in English

⚙️ Tech Stack

• Languages: Python
• ML & Data: Scikit-learn, Pandas (nice to have: TensorFlow, PyTorch)
• MLOps: MLflow, Kubeflow (or similar)
• Cloud: AWS (SageMaker, S3, ML services)
• Containers & Orchestration: Docker, Kubernetes
• APIs & Services: REST APIs (FastAPI / Flask)
• Data: SQL / NoSQL
• Infrastructure: (nice to have) Terraform, CloudFormation
• Observability: Model and pipeline monitoring (various tools)

💡 What you will find

• Projects with a strong focus on data and Machine Learning in production
• Focus on engineering, scalability, and best development practices
• Collaborative environment with Data Engineers and Data Scientists
• Culture of technical decision-making and continuous improvement
• Flexible work model