ML Engineer

We are seeking a skilled Machine Learning Engineer to design, build, and deploy scalable ML systems that power intelligent features and data-driven decision-making. You will work at the intersection of data science and software engineering—transforming prototypes into production-ready systems while ensuring reliability, performance, and fairness.

Key Responsibilities

Data Engineering & Preparation:

Collect, clean, and preprocess structured and unstructured data from multiple sources.

Build scalable data pipelines for feature extraction, validation, and transformation.

Implement monitoring for data quality and consistency.

Model Development:

Select and implement machine learning algorithms and architectures suitable for specific business problems.

Perform feature engineering, model training, and hyperparameter optimization.

Deployment & Productionization:

Package and deploy models as APIs, batch processes, or streaming services.

Containerize and orchestrate ML systems using Docker, Kubernetes, or cloud ML platforms.

Optimize inference performance for latency, throughput, and resource efficiency.

MLOps & Lifecycle Management:

Build and maintain CI/CD pipelines for ML workflows.

Implement model versioning, retraining pipelines, and monitoring systems.

Detect and mitigate model drift, data drift, and performance degradation.

Collaboration & Communication

Partner with software engineers and product teams to integrate ML into larger applications.

Communicate model behavior, trade-offs, and limitations to technical and non-technical stakeholders.

Document workflows, best practices, and design decisions.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.

1–4 years of hands-on experience in developing and deploying ML models.

Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).

Strong background in algorithms, data structures, and software engineering practices.

Experience with cloud ML platforms (AWS Sagemaker, GCP Vertex AI, Azure ML) and containerization (Docker, Kubernetes).

Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, CI/CD).

Strong problem-solving skills and ability to translate business needs into technical solutions.