AI/ML Engineer

Role Overview

• Design, develop, and deploy machine learning and artificial intelligence solutions that drive product innovation and business impact
• Work closely with engineering, data, product, and business teams to build scalable models, production-grade ML systems, and intelligent applications
• Handle the full machine learning lifecycle, from data preparation and feature engineering to model training, deployment, monitoring, and continuous improvement
• Combine strong technical depth with practical problem-solving skills and collaborate effectively with both technical and non-technical stakeholders in a remote environment aligned with U.S. time zones

Requirements

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, or a related quantitative field
• Minimum 5 years of professional experience in machine learning, artificial intelligence, or software engineering roles with a strong machine learning focus
• Strong proficiency in Python and hands-on experience with machine learning libraries and frameworks such as scikit-learn, TensorFlow, PyTorch, Pandas, and NumPy
• Proven experience building, evaluating, deploying, and supporting machine learning models in production environments
• Strong understanding of supervised and unsupervised learning, feature engineering, model evaluation, and statistical analysis
• Experience designing and maintaining data pipelines and machine learning workflows in cloud environments such as AWS, Microsoft Azure, or Google Cloud Platform
• Familiarity with APIs, microservices, and backend engineering concepts for integrating machine learning solutions into production systems
• Strong SQL skills and experience working with large-scale structured and unstructured datasets
• Strong written and verbal communication skills in English, with the ability to explain complex technical concepts clearly to technical and non-technical stakeholders
• Demonstrated ability to work effectively in a remote environment while aligned with U.S. time zones
• Preferred experience with MLOps tools and practices such as MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, model monitoring, CI/CD, and lifecycle management
• Preferred experience with natural language processing, computer vision, recommender systems, or time-series forecasting
• Preferred familiarity with distributed processing technologies such as Spark, Hadoop, or Ray
• Preferred experience with vector databases, embeddings, retrieval pipelines, and large language model integrations
• Preferred exposure to generative AI frameworks and tools such as LangChain, LangGraph, Hugging Face, and OpenAI APIs
• Preferred experience building scalable inference services using Docker, Kubernetes, and cloud-native deployment patterns
• Preferred knowledge of data governance, model explainability, responsible AI practices, and model risk management
• Preferred experience working in fast-paced, cross-functional product or platform teams

Tech Stack

• AWS
• Azure
• Cloud
• Docker
• Google Cloud Platform
• Hadoop
• Kubernetes
• Microservices
• Numpy
• Pandas
• Python
• PyTorch
• Ray
• Scikit-Learn
• Spark
• SQL
• Tensorflow

Benefits

• Competitive compensation
• Flexible remote work aligned with U.S. time zones
• Opportunity to work on innovative AI and machine learning initiatives with meaningful business impact
• Collaborative, technically strong, and forward-looking engineering environment
• Long-term career growth and professional development opportunities