Associate Machine Learning Engineer

Summary

Join Encora's growing AI/ML team as a highly motivated Associate Machine Learning Engineer with 2–5 years of experience. You will play a key role in building, deploying, and scaling machine learning models for real-world applications. You will collaborate with data scientists, engineers, and product managers. This 6-month project offers high potential for extension and is remote, supporting EST hours. The ideal candidate is comfortable with data pipelines, model development, and MLOps workflows, contributing to both experimentation and production systems.

Requirements

• 2–5 years of experience in ML engineering, applied machine learning, or related roles
• Strong proficiency in Python and libraries such as scikit-learn, Pandas, NumPy, and PyTorch or TensorFlow
• Solid understanding of machine learning fundamentals including model evaluation, overfitting, regularization, etc
• Experience with ML workflow tools like MLflow, Airflow, or Kubeflow
• Familiarity with cloud platforms such as AWS (SageMaker), GCP (Vertex AI), or Azure (ML Studio)
• Experience working with version control (Git) and CI/CD practices
• Excellent problem-solving and collaboration skills

Responsibilities

• Collaborate with data scientists, engineers, and product managers to design, build, and deploy ML models into production
• Develop and maintain robust, scalable pipelines for data preprocessing, feature engineering, and model training
• Integrate models with production systems and monitor their performance post-deployment
• Participate in code reviews, testing, and documentation to ensure high-quality, maintainable ML code
• Contribute to model versioning, experiment tracking, and reproducibility using tools like MLflow, Weights & Biases, or similar
• Implement model validation techniques, A/B testing, and continuous model improvement practices
• Help optimize model performance and infrastructure costs across cloud or on-prem environments

Preferred Qualifications

• Experience deploying models in real-time or batch environments via REST APIs, containers, or streaming platforms (e.g., Kafka)
• Knowledge of MLOps tools and concepts such as Docker, Kubernetes, model registries, and feature stores
• Familiarity with big data technologies (e.g., Spark, Hadoop) is a plus
• Exposure to NLP, computer vision, or time series modeling is a bonus
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field