Machine Learning Engineer

Join Hire Hangar and work with fast-growing global companies while building a long-term, remote career.Machine Learning Engineer (Data & AI)RemoteUS Time Zones (EST–PST)Role OverviewWe are looking for a skilled Machine Learning Engineer with a strong data engineering foundation to build, train, and deploy ML models and data pipelines across a range of complex environments. This role sits at the intersection of data and AI — you will be responsible for everything from sourcing, cleaning, and structuring data to training models, evaluating performance, and getting solutions into production. The ideal candidate thinks rigorously about data quality, understands the full ML lifecycle, and is equally comfortable working with large datasets as they are fine-tuning models or building scalable inference pipelines.Key ResponsibilitiesDesign, build, and maintain robust data pipelines for ingestion, transformation, and feature engineeringDevelop, train, evaluate, and iterate on machine learning models across classification, regression, clustering, and NLP tasksFine-tune and adapt pre-trained LLMs and foundation models for specific use cases and datasetsBuild and manage MLOps infrastructure including model versioning, experiment tracking, and deployment pipelinesWork with structured and unstructured data at scale — including text, tabular, and time-series dataMonitor model performance in production and implement retraining and drift-detection strategiesCollaborate with engineering and product teams to translate data insights into actionable AI featuresDocument data schemas, model architectures, and pipeline logic clearly and thoroughlyRequired QualificationsStrong 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 datasetsPractical experience with model training, evaluation, hyperparameter tuning, and deploymentFamiliarity with LLMs and transformer-based architectures; experience with fine-tuning or prompt engineering in production contextsExperience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC, or similar)Strong grasp of statistical concepts, data quality principles, and model performance metricsMust 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.Preferred QualificationsExperience with distributed data processing frameworks (Spark, Dask, or similar)Familiarity with vector databases and embedding-based retrieval systemsBackground 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 workflowsTools & TechnologyPython, SQL, and core ML/data libraries (PyTorch, scikit-learn, Pandas, NumPy)MLOps: MLflow, Weights & Biases, DVC, or equivalentData warehouses and lakes: Snowflake, BigQuery, Redshift, or similarLLM platforms: Hugging Face, OpenAI, Anthropic, or similarCloud infrastructure: AWS, GCP, or AzureGoogle Workspace, Slack, Zoom, and remote collaboration toolsPlease note: It is crucial that you complete the application form in full. As part of the application process, you will be required to record a video. If your application is successful, you will receive an email confirming next steps — the video is the first step of the interview process. If you do not record a video, we will not be able to consider you for ANY open roles.We connect top talent with vetted employers, competitive pay, and real growth opportunities.Originally posted on Himalayas