We are seeking a skilled Machine Learning Engineer with 3–4 years of hands-on experience in building, deploying, and optimizing machine learning models in production environments. The ideal candidate will bridge the gap between data science and software engineering to create scalable AI-driven solutions.
Design, develop, and deploy machine learning models into production
Build scalable ML pipelines for training, validation, and inference
Collaborate with Data Scientists, Data Engineers, and Product teams
Optimize model performance, scalability, and reliability
Implement model monitoring, retraining, and versioning processes
Work with structured and unstructured datasets
Conduct feature engineering and data preprocessing
Ensure best practices in code quality, testing, and documentation
Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, or related field
3–4 years of experience in ML engineering or applied machine learning
Strong programming skills in Python
Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn
Experience with deep learning, NLP, or computer vision (depending on use case)
Strong understanding of algorithms, data structures, and system design
Experience writing production-grade code and REST APIs
Proficiency in SQL and working with large datasets
Experience with containerization tools like Docker
Familiarity with orchestration tools such as Kubernetes
Experience with CI/CD pipelines
Knowledge of ML lifecycle tools like MLflow or Kubeflow
Experience deploying models on cloud platforms like Amazon Web Services, Google Cloud Platform, or Microsoft Azure
Experience with big data tools such as Spark
Knowledge of distributed computing
Experience with real-time inference systems
Exposure to LLMs and transformer-based architectures
Prior experience in a product-based or high-scale environment