Machine Learning Engineer

Itron

1 - 3 years

10 - 14 Lacs

bengaluru

Posted:-1 days ago| Platform:

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Skills Required

computer science version control orchestration git gcp artificial intelligence machine learning debugging sql python

Work Mode

Work from Office

Job Type

Full Time

Job Description

Collaborate with data scientists and product teams to productionize machine learning models and ensure scalability, reliability, and performance.
Design, build, and maintain data pipelines to support model training and inference using tools like Spark, Airflow, or Kubeflow.
Implement model serving infrastructure, APIs, and CI/CD workflows for automated model deployment and updates.
Optimize model performance for latency, throughput, and cost efficiency in production environments.
Integrate ML solutions into applications and services, working closely with software engineers.
Monitor and maintain ML systems to ensure data integrity, model drift detection, and reliable predictions.
Contribute to MLOps best practices including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow, Weights & Biases).
Collaborate on architecture design for scalable ML systems on cloud platforms (AWS, GCP, Azure).
Stay current with advances in ML systems, frameworks, and cloud technologies.
Who You Are
Education: Bachelor s, Master s, or PhD in Computer Science, Engineering, or a related technical field. Experience: Level I: 1-3 years of experience building or deploying ML models. Proficient in programming languages such as Python or Java, with experience in ML libraries like TensorFlow, PyTorch, or scikit-learn. Strong software engineering skills clean, modular code; version control (Git); testing; and documentation. Experience with data infrastructure (SQL, Spark, Kafka, Airflow, or similar). Hands-on experience with containerization and orchestration (Docker, Kubernetes). Familiarity with CI/CD pipelines for ML deployment (Jenkins, GitHub Actions, etc.). Cloud experience with AWS SageMaker, GCP Vertex AI, or Azure ML is highly preferred. Understanding of MLOps concepts, model monitoring, and lifecycle management.

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