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Machine Learning Engineer – AWS

Digital Biz Solutions

Kochi · On-site Full-time Yesterday

About the role

Job Summary

We are seeking a highly skilled and motivated Machine Learning Engineer with a strong foundation in programming and machine learning, hands‑on experience with AWS Machine Learning services (especially SageMaker), and a solid understanding of Data Engineering and MLOps practices. You will be responsible for designing, developing, deploying, and maintaining scalable ML solutions in a cloud‑native environment.

Key Responsibilities

  • Design and implement machine learning models and pipelines using AWS SageMaker and related services.
  • Develop and maintain robust data pipelines for training and inference workflows.
  • Collaborate with data scientists, engineers, and product teams to translate business requirements into ML solutions.
  • Implement MLOps best practices including CI/CD for ML, model versioning, monitoring, and retraining strategies.
  • Optimize model performance and ensure scalability and reliability in production environments.
  • Monitor deployed models for drift, performance degradation, and anomalies.
  • Document processes, architectures, and workflows for reproducibility and compliance.

Required Skills & Qualifications

  • Strong programming skills in Python and familiarity with ML libraries (e.g., scikit‑learn, TensorFlow, PyTorch).
  • Solid understanding of machine learning algorithms, model evaluation, and tuning.
  • Hands‑on experience with AWS ML services, especially SageMaker, S3, Lambda, Step Functions, and CloudWatch.
  • Experience with data engineering tools (e.g., Apache Airflow, Spark, Glue) and workflow orchestration.
  • Proficiency in MLOps tools and practices (e.g., MLflow, Kubeflow, CI/CD pipelines, Docker, Kubernetes).
  • Familiarity with monitoring tools and logging frameworks for ML systems.
  • Excellent problem‑solving and communication skills.

Preferred Qualifications

  • AWS Certification (e.g., AWS Certified Machine Learning – Specialty).
  • Experience with real‑time inference and streaming data.
  • Knowledge of data governance, security, and compliance in ML systems.

Unique Job ID

NA

Requirements

  • Strong programming skills in Python and familiarity with ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
  • Solid understanding of machine learning algorithms, model evaluation, and tuning.
  • Hands-on experience with AWS ML services, especially SageMaker, S3, Lambda, Step Functions, and CloudWatch.
  • Experience with data engineering tools (e.g., Apache Airflow, Spark, Glue) and workflow orchestration.
  • Proficiency in MLOps tools and practices (e.g., MLflow, Kubeflow, CI/CD pipelines, Docker, Kubernetes).
  • Familiarity with monitoring tools and logging frameworks for ML systems.
  • Excellent problem-solving and communication skills.

Responsibilities

  • Design and implement machine learning models and pipelines using AWS SageMaker and related services.
  • Develop and maintain robust data pipelines for training and inference workflows.
  • Collaborate with data scientists, engineers, and product teams to translate business requirements into ML solutions.
  • Implement MLOps best practices including CI/CD for ML, model versioning, monitoring, and retraining strategies.
  • Optimize model performance and ensure scalability and reliability in production environments.
  • Monitor deployed models for drift, performance degradation, and anomalies.
  • Document processes, architectures, and workflows for reproducibility and compliance.

Skills

AWS CloudWatchAWS LambdaAWS SageMakerAWS Step FunctionsAWS S3Apache AirflowCI/CDDockerKubernetesMLflowMLOpsPyTorchPythonSparkTensorFlowscikit-learn

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