ML Engineer (MLOps)

Experience: 0–5 Years

Location: Pan India

Role Overview

We 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 Responsibilities

- Develop, 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 Skills
- Strong Python programming.
- Machine Learning fundamentals: regression, classification, clustering, feature engineering and model evaluation.

Machine Learning Models




- Ability 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: team-oriented 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 Skills
- MLflow 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 Stack
- Python
- Scikit-learn
- PyTorch / TensorFlow
- MLflow
- DVC
- 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
- Linux

Ideal Candidate Profile The 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 Skills

Python | 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 | Linux

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