Machine Learning Engineer

Role Overview

We are looking for a Machine Learning Engineer to build and deploy ML models supporting the Linkfields platform, including use cases such as recommendations, demand forecasting, and anomaly detection, and productionize them at scale on AWS.

Key Responsibilities

• Design, train, and evaluate ML models for relevant business use cases.
• Build production ML pipelines covering feature engineering, training, deployment, and monitoring.
• Deploy models using AWS SageMaker, Lambda, or containerized services.
• Implement MLOps practices including model CI/CD, monitoring, and retraining.
• Collaborate with Data Engineers on data pipelines and feature stores.
• Document model performance, limitations, and assumptions.

Required Skills & Experience

• 5+ years of experience building and deploying ML models.
• Strong Python skills and experience with ML frameworks such as scikit-learn, PyTorch, or TensorFlow.
• Experience productionizing ML models at scale.
• Hands-on experience with the AWS ML stack, particularly SageMaker or equivalent.
• Experience with data pipelines and feature engineering.

Nice to Have

• MLOps tooling experience with MLflow, Kubeflow, or similar platforms.
• NLP or LLM fine-tuning experience.
• Telecom or retail use-case experience.

What Were Looking For

Were looking for an ML Engineer with strong problem-solving skills, ownership, curiosity, and the ability to collaborate effectively with Data Engineering and cross-functional technical teams.

Why Join Intelicore Systems?

• Work on global enterprise AI/ML projects.
• Gain hands-on experience with AWS, ML, and MLOps technologies.
• Work on real-world use cases such as recommendations, forecasting, and anomaly detection.
• Collaborate with experienced technical and business teams.
• Solve meaningful business challenges using modern AI/ML solutions.

Interested? Apply now or share your profile with us.

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