Machine Learning Engineer

Position Title:

Experience:

Location:

Description:

Machine Learning Engineer

You will work closely with cross-functional teams to transform business problems into ML solutions, optimize models for performance, and deploy them seamlessly using cloud-native tools.

Key Responsibilities:

• Build and maintain

  feature/data pipelines

  using

  PySpark

  and Python.
• Perform

  Exploratory Data Analysis (EDA)

  and feature engineering.
• Design and implement

  ML models

  — regression, forecasting, NLP, and image/video analytics.
• Apply

  hyperparameter tuning

  , model performance evaluation, and deployment best practices.
• Implement

  MLOps pipelines

  for continuous integration and deployment.
• Leverage

  AWS services

  — SageMaker, Bedrock, Kendra, and other ML tools.
• Collaborate with data engineers, data scientists, and business analysts to generate actionable insights.
• Contribute to solution architecture, code reviews, and ML lifecycle management.
• Write clean, reusable, and efficient code with proper unit tests.
• Drive knowledge sharing and continuous improvement across the ML team.

Required Skills:

• Strong proficiency in

  Python

  (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow).
• 3+ years of experience in

  PySpark-based data pipelines

  .
• Sound understanding of

  statistics

  (probability, hypothesis testing, distributions).
• Experience with

  MLOps tools and ML model lifecycle management

  .
• Familiarity with

  AWS ML stack

  (SageMaker, Bedrock, Kendra).
• Knowledge of

  model deployment

  , monitoring, and scaling.
• Experience in

  time-series forecasting

  ,

  NLP

  , and

  image/video analytics

  .

Good To Have:

• Experience with

  Generative AI / LLMs

  (LangChain, LlamaIndex, foundation model fine-tuning).
• Understanding of

  Docker, Kubernetes

  , and CI/CD for ML workflows.
• Background in

  data engineering

  or analytics model integration.