Machine Learning Engineer (0-5)

Machine Learning Engineer (0-5)

Posted by Fortune 500 IT Services Company

Noida,Pune,Bengaluru

Posted: imported by Jeevika

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HybridNoida, Pune, BengaluruPosted: 2 days agoOpenings: 2Applicants: 100+Machine Learning Engineer (0-5)Tech Mahindra3.347K ReviewsJob description ML Engineer (MLOps) Job DescriptionExperience: 0–5 YearsLocation: Pan IndiaRole OverviewWe are looking for an ML Engineer who can work across Machine Learning model development and the operational lifecycle of ML systems. The role combines model development, training, evaluation, deployment, CI/CD, infrastructure automation, monitoring and production support, with a strong preference for open-source tooling.Key ResponsibilitiesDevelop, train, validate and optimize Machine Learning models.Perform data preprocessing, feature engineering and model evaluation.Build reproducible ML training and inference workflows.Package and deploy ML models as production services.Design ML pipelines covering data, training, validation, deployment and monitoring.Implement CI/CD and automation for ML applications and models.Manage model versions, experiments, artifacts and release processes.Containerize workloads using Docker and deploy them on suitable infrastructure.Implement model/application logging, metrics, monitoring and alerting.Optimize inference latency, resource utilization and scalability.Work with Kubernetes and container orchestration where applicable.Collaborate with AI Engineers, Data Engineers and Full Stack Developers.Deploy, serve, and manage machine learning models across development and production environments, including model versioning and lifecycle management.Build and maintain CI/CD pipelines to automate model deployment and ML application releases.Core Technical SkillsStrong Python programming.Machine Learning fundamentals: regression, classification, clustering, feature engineering and model evaluation.Machine Learning ModelsAbility to select the appropriate model based on the business problem, dataset characteristics, performance requirements, and production constraints.Feature Engineering, Feature Selection, Hyperparameter Tuning, Model Optimization, and Model Explainability.Model Selection & Evaluation: cross-validation, precision, recall, F1-score, ROC-AUC, confusion matrix, RMSE, MAE, and appropriate business metrics.Dimensionality Reduction: PCA and related techniques.Recommendation Systems: collaborative filtering, content-based approaches, and hybrid recommendation techniques.Time-Series: forecasting, trend/seasonality analysis, and time-series model evaluation.Clustering: K-Means, DBSCAN, hierarchical clustering, and related methods.Boosting: XGBoost, LightGBM, and similar gradient-boosting techniques.Classification: Logistic Regression, Decision Trees, Random Forest, SVM, KNN, Naive Bayes, and ensemble methods.Regression: Linear Regression, Ridge/Lasso, and related approaches.Scikit-learn and practical ML development experience.PyTorch and/or TensorFlow.Git, Linux, shell scripting and software engineering practices.REST APIs and model-serving concepts.Docker and CI/CD fundamentals.SQL and basic data pipeline knowledge.Understanding of cloud or infrastructure concepts.Preferred / Additional SkillsMLflow for experiment tracking, model registry and lifecycle management.Kubeflow, KServe or Seldon for ML workflows/serving.Apache Airflow, Prefect or Dagster for workflow orchestration.Kubernetes and Helm.Terraform or OpenTofu for infrastructure as code.Prometheus and Grafana for monitoring.OpenTelemetry, Loki and/or ELK/OpenSearch for observability.Argo CD / Argo Workflows or GitLab CI for automation.Feast or another open-source feature store.Kafka for event-driven ML/data workflows.DVC, AWS Sagemaker, AWS Bedrock Open-Source Technology StackPythonScikit-learnPyTorch / TensorFlowMLflowDVCKubeflow / KServe / SeldonApache Airflow / Prefect / DagsterDockerKubernetesHelmTerraform / OpenTofuGitHub Actions / GitLab CI / JenkinsArgo CD / Argo WorkflowsPrometheus + GrafanaOpenTelemetryPostgreSQLRedisKafkaLinuxIdeal Candidate ProfileThe ideal candidate should be capable of moving an ML model from experimentation to a reliable production service, while also handling automation, deployment, observability and lifecycle management. This is a hybrid ML + MLOps role rather than a pure DevOps position.Key SkillsPython Scikit-learn PyTorch / TensorFlow MLflow Kubeflow / KServe / Seldon Apache Airflow / Prefect / Dagster Docker Kubernetes Helm Terraform / OpenTofu GitHub Actions / GitLab CI / Jenkins Argo CD / Argo Workflows Prometheus + Grafana OpenTelemetry PostgreSQL Redis Kafka LinuxRole: Software Development - Other,Industry Type: IT Services & Consulting,Department: Engineering - Software & QA,Employment Type: Full Time, PermanentRole Category: Software DevelopmentEducationUG: Any GraduateKey SkillsSkills highlighted with ‘‘ are preferred keyskillsTensorflowMachine learning developementOpen Source TechnologiesCI/CDMachine Learning OperationsPytorchML PipelinesPrometheusAWSScikit-LearnSQLBeware of imposters! Naukri.com does not promise a job or an interview in exchange of money. 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