Associate Machine Learning Engineer

Encora Inc.; Indianapolis (IN); Remote; USD 80,000 - 110,000; Full time

Job summary

A forward-thinking company is seeking a motivated Associate Machine Learning Engineer to join their dynamic AI/ML team. In this role, you will be instrumental in building and scaling machine learning models that drive impactful real-world applications. You'll work with cutting-edge technologies, collaborate with diverse teams, and contribute to both experimentation and production-grade systems. This position offers the flexibility of remote work while supporting EST hours, making it an exciting opportunity for those passionate about machine learning and innovation.

Qualifications

• 2-5 years of experience in ML engineering or applied machine learning.
• Strong proficiency in Python and machine learning libraries.

Responsibilities

• Collaborate with teams to design and deploy ML models.
• Develop scalable pipelines for data preprocessing and model training.

Skills

Python

scikit-learn

Pandas

NumPy

PyTorch

TensorFlow

MLflow

Airflow

Kubeflow

Git

Education

Bachelor's degree in Computer Science

Master's degree in Data Science

Tools

AWS SageMaker

GCP Vertex AI

Azure ML Studio

Docker

Kubernetes

Spark

Hadoop

Job description

Encora is seeking a highly motivated Associate Machine Learning Engineer with 2–5 years of experience to join our growing AI/ML team. You will play a key role in building, deploying, and scaling machine learning models that power real-world applications. The ideal candidate is equally comfortable working with data pipelines, model development, and MLOps workflows—and is excited to contribute to both experimentation and production-grade systems. This is a 6 month project with high potential for extension. You will work remote, supporting EST hours.

Key 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.
  
  

Required Qualifications:

• 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.
  
  

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.