Note: The job is a remote job and is open to candidates in USA. Hire Hangar connects top talent with vetted employers, competitive pay, and growth opportunities. The Machine Learning Engineer will build, train, evaluate, and deploy machine learning models and scalable data pipelines across complex environments. The role also involves MLOps infrastructure, production monitoring, data quality, and collaboration with engineering and product teams to develop AI features.
Responsibilities
- Design, build, and maintain robust data pipelines for ingestion, transformation, and feature engineering
- Develop, train, evaluate, and iterate on machine learning models across classification, regression, clustering, and NLP tasks
- Fine-tune and adapt pre-trained LLMs and foundation models for specific use cases and datasets
- Build and manage MLOps infrastructure including model versioning, experiment tracking, and deployment pipelines
- Work with structured and unstructured data at scale — including text, tabular, and time-series data
- Monitor model performance in production and implement retraining and drift-detection strategies
- Collaborate with engineering and product teams to translate data insights into actionable AI features
- Document data schemas, model architectures, and pipeline logic clearly and thoroughly
Skills
- Strong Python skills with hands-on experience in core ML libraries (scikit-learn, PyTorch, TensorFlow, or similar)
- Solid data engineering experience — SQL, ETL pipelines, and working with large-scale datasets
- Practical experience with model training, evaluation, hyperparameter tuning, and deployment
- Familiarity with LLMs and transformer-based architectures; experience with fine-tuning or prompt engineering in production contexts
- Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC, or similar)
- Strong grasp of statistical concepts, data quality principles, and model performance metrics
- Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered
- Experience with distributed data processing frameworks (Spark, Dask, or similar)
- Familiarity with vector databases and embedding-based retrieval systems
- Background working with real-time or streaming data pipelines (Kafka, Flink, or similar)
- Exposure to cloud-native ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
- Experience with data governance, lineage tracking, or compliance-aware data workflows
Benefits
- Remote work
- Real growth opportunities
Company Overview