ML Engineer

About the Role:

Machine Learning Engineer

Key Responsibilities:

• Build and validate ML prototypes to solve real business problems
• Develop, test, and optimize ML models using structured and unstructured data
• Design and implement scalable data pipelines and model serving infrastructure
• Continuously monitor, improve, and re-train models in production
• Ensure reproducibility, versioning, and documentation of models and experiments
• Evaluate and select appropriate ML tools, frameworks, and technologies to meet business requirements.
• Oversee the full ML lifecycle including data preparation, model development, training, validation, deployment, and monitoring.
• Collaborate with stakeholders to translate business needs into ML solutions.

Required Skills & Qualifications:

• Bachelor s or Master s degree in Computer Science, Data Science, AI/ML, or related field.
• 5+ years of professional experience with at least 3 years in machine learning and data science.
• Take at least one ML solution from idea to full-scale production deployment
• Strong programming skills in

  Python

  , with experience in ML libraries like

  TensorFlow, PyTorch, Scikit-learn, XGBoost

  , etc.
• Deep understanding of

  ML architecture patterns

  ,

  data pipelines

  , and

  distributed systems

  .
• Experience with

  any of cloud

  platforms like

  AWS, Azure, or GCP

  and cloud-native ML tools (SageMaker, Vertex AI, Azure ML).
• Proficiency in

  Docker

  ,

  Kubernetes

  , and other containerization/orchestration tools.
• Strong grasp of

  MLOps

  ,

  model monitoring

  , and

  continuous integration/deployment

  pipelines.
• Hands-on experience with big data technologies like

  Spark, Hadoop, Hive

  , or similar.

Preferred Qualifications:

Contributions to open-source ML projects or research publications.

deep learning

data privacy