Machine Learning Engineer

Key Responsibilities

Model Development: Build, train, and optimize machine learning models for real-world applications (classification, regression, NLP, recommendation, forecasting, etc.).

Data Engineering: Work with large structured and unstructured datasets; clean, preprocess, and feature engineer for ML pipelines.

Deployment & Scaling: Package models into APIs or services, and deploy them into cloud or on-prem production environments.

MLOps: Implement monitoring, retraining, and performance tuning to ensure reliability of deployed models.

Collaboration: Work closely with data scientists (research focus) and software engineers (production systems) to bridge experimentation and deployment.

Documentation: Maintain clear documentation of data sources, model design decisions, and deployment processes.

Continuous Learning: Stay up to date with the latest ML frameworks, tools, and best practices.

Qualifications

Required

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
• 3–5 years of experience in applied machine learning or AI engineering.
• Proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch, XGBoost, etc.).
• Strong SQL and data wrangling skills.
• Experience deploying ML models in production (e.g., REST APIs, Docker, Kubernetes, or cloud ML services such as AWS SageMaker, GCP Vertex AI, Azure ML).
• Knowledge of software engineering practices (Git, CI/CD, testing).
• Strong problem-solving and communication skills.

Preferred

• Experience with NLP, computer vision, or time series modeling.
• Familiarity with MLOps frameworks (MLflow, Kubeflow, Airflow).
• Experience in insert healthcare, retail or pharmaceutical industry.

What We Offer

• Competitive salary and performance-based bonus.
• Professional development opportunities.
• Flexible hybrid/remote work options.
• A collaborative, innovative environment where you can have real impact.