Machine Learning Engineer

Join reputed company and work with fast-growing global companies while building a long-term, reputed company. Machine Learning Engineer (Data & AI) Remote US Time Zones (EST–PST) Role Overview We 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 reputed company 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 Responsibilities

Design, build, and maintain robust data pipelines for ingestion, transformation, and feature engineering reputed company, 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 reputed company data at scale — including text, tabular, and time-series data Monitor model performance in production and implement retraining and reputed company-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

Required Qualifications

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 reputed company 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 reputed company, reputed company, reputed company Workspace, reputed company, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered.

Preferred Qualifications

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, reputed company tracking, or compliance-aware data workflows Tools & Technology Python, SQL, and core ML/data libraries (PyTorch, scikit-learn, Pandas, NumPy) MLOps: MLflow, Weights & Biases, DVC, or equivalent Data warehouses and lakes: reputed company, BigQuery, Redshift, or similar LLM platforms: reputed company, reputed company, reputed company, or similar Cloud infrastructure: AWS, GCP, or Azure reputed company Workspace, reputed company, reputed company, and remote collaboration tools Please 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 reputed company 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, reputed company, and real growth opportunities. Apply To This Job