Machine Learning Engineer – Cloud | MLOps | GenAI (Relocation provided)
Key Responsibilities
- Designing and implementing machine learning pipelines in Azure cloud environment
- Developing and deploying models for classification, regression, time series, recommendation, or NLP use cases
- Working with structured and unstructured data, and applying feature engineering, model tuning, and evaluation techniques
- Packaging and deploying models using containerization (e.g., Docker, Kubernetes)
- Automating and monitoring ML workflows using MLflow, Airflow, or cloud-native tools (SageMaker, Vertex AI, Azure ML)
- Collaborating with data scientists, engineers, and product teams to translate business problems into ML solutions
- Contributing to MLOps practices: model versioning, CI/CD for ML, performance monitoring, and rollback strategies
Requirements
- At least 5 years of professional experience.
- Advanced proficiency in English (minimum B2 level in speaking, writing, listening, and reading).
- A bachelor’s degree in Computer Science, Software Engineering, or a related field.
- Strong proficiency in Python, including libraries such as scikit-learn, TensorFlow, PyTorch, or XGBoost
- Experience building and deploying ML models in at least one major cloud platform: Azure, AWS, or GCP
- Familiarity with ML pipeline orchestration and CI/CD practices
- Experience with Generative AI use cases, such as working with LLMs, embedding models, prompt engineering, or custom GPT integrations
- Familiarity with HuggingFace Transformers, LangChain, or RAG architectures
- Exposure to Responsible AI, explainability, or ethical ML practices
- Background in MLOps tooling: MLflow, DVC, Tecton, Feast
- Experience with data labeling tools or ML observability platforms
- Solid understanding of software engineering principles and cloud infrastructure
- Experience working with APIs, data lakes, and distributed systems is a plus
- Comfortable in Agile environments and cross-functional teams
What We Offer
- Competitive Compensation: Salary tailored to your skills and experience.
- Generous Time Off: 25 paid vacation days (based on a 40-hour work week).
- Relocation Support: A relocation and travel budget to ease your transition.
- Tax Benefits: Eligibility for the 30% ruling.
- Travel Reimbursement: We've got your daily commute covered.
- Top-Notch Equipment: A high-quality device to ensure you work efficiently.
- Professional Development: Financial support for certifications, technical training, and language courses—because we value your long-term career growth.
- Family: Visa sponsorship for your partner and children under 18.
- Temporary Accommodation: Stay in temporary housing while we help you find your permanent home.
- Comprehensive Relocation Assistance: Extensive support to help you settle in smoothly.
- Community & Fun: Enjoy team events and connect with developers from around the globe!
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Skills
- Agile
- AI
- Airflow
- Analytics
- API
- AWS
- Azure
- Bash
- CI/CD
- Cloud
- Cloud Native
- Containerization
- Distributed Systems
- Docker
- Feature Engineering
- GCP
- Generative AI
- Hugging Face
- Kubernetes
- LangChain
- LLM
- Machine Learning
- MLflow
- MLOps
- NLP
- Observability
- Predictive Analytics
- Prompt Engineering
- Python
- PyTorch
- RAG
- SageMaker
- scikit-learn
- TensorFlow
- Time Series
- Transformers
- Vertex AI
- XGBoost