Machine Learning Engineer

Machine Learning Engineer

Key Responsibilities

• Design, develop, and optimize

  machine learning models

  for production use.
• Build scalable

  data pipelines

  for model training, testing, and deployment.
• Collaborate with

  data scientists

  to transition research models into production-grade solutions.
• Implement and manage

  MLOps practices

  (CI/CD, model monitoring, retraining pipelines).
• Work with large-scale structured and unstructured datasets.
• Conduct

  feature engineering

  and evaluate model performance with robust metrics.
• Deploy models into

  cloud or on-prem environments

  (AWS, GCP, Azure, Kubernetes, Docker).
• Stay updated with the latest trends in ML/AI and recommend adoption where relevant.

Required Skills & Qualifications

• Bachelor’s/Master’s degree in

  Computer Science, Data Science, Mathematics, or related field

  .
• Strong proficiency in

  Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow, Keras)

  .
• Experience with

  MLOps tools

  (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI).
• Good knowledge of

  data structures, algorithms, and software engineering best practices

  .
• Hands-on experience in

  building and deploying ML models

  in production.
• Familiarity with

  cloud platforms

  (AWS/GCP/Azure).
• Proficiency with

  SQL/NoSQL databases

  and data pipelines.

Nice-to-Have Skills

• Experience in

  NLP, Computer Vision, or Generative AI

  .
• Knowledge of

  big data frameworks

  (Spark, Hadoop).
• Exposure to

  A/B testing, recommendation systems, or real-time ML inference

  .
• Contributions to open-source ML projects.