Associate Machine Learning Engineer

Encora Inc.; Indianapolis (IN); Remote; USD 70,000 - 110,000; Full time

Job summary

An innovative firm is on the lookout for a motivated Associate Machine Learning Engineer to join their dynamic AI/ML team. This exciting role involves building, deploying, and scaling machine learning models that drive real-world applications. You'll work closely with data scientists and engineers to create robust data pipelines and integrate models into production systems. If you're passionate about machine learning and eager to contribute to both experimentation and production-grade systems, this opportunity offers a great platform for growth and collaboration in a remote work environment.

Qualifications

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

Responsibilities

• Collaborate with teams to design, build, 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 in Computer Science

Master's in Computer Science

Tools

AWS SageMaker

GCP Vertex AI

Azure ML Studio

Docker

Kubernetes

MLflow

Job description

Join to apply for the Associate Machine Learning Engineer role at Encora Inc.

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 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 remotely, supporting EST hours.

Key Responsibilities:

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

Required Qualifications:

1. 2–5 years of experience in ML engineering, applied machine learning, or related roles.
2. Strong proficiency in Python and libraries such as scikit-learn, Pandas, NumPy, and PyTorch or TensorFlow.
3. Solid understanding of machine learning fundamentals including model evaluation, overfitting, regularization, etc.
4. Experience with ML workflow tools like MLflow, Airflow, or Kubeflow.
5. Familiarity with cloud platforms such as AWS (SageMaker), GCP (Vertex AI), or Azure (ML Studio).
6. Experience working with version control (Git) and CI/CD practices.
7. Excellent problem-solving and collaboration skills.

Preferred Qualifications:

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

Additional Details:

• Seniority level: Entry level
• Employment type: Full-time
• Job function: Engineering and Information Technology
• Industries: IT Services and IT Consulting