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Conventional AI / ML Engineers

Role:

Conventional AI / ML Engineers

Location:

Indore CITP, Pune - Baner, Pune - Viman Nagar

Experience:

2+ years

Key Skills:

AI-ML, Deep Learning


What will your role look like
  • Develop, train, and deploy machine learning and deep learning models across diverse business domains.
  • Work on data preprocessing, feature engineering, model experimentation, and evaluation.
  • Support model deployment, monitoring, and MLOps workflows to ensure stable production performance.
  • Collaborate with product, data, and engineering teams to translate insights into scalable, deployable ML solutions.
  • Contribute to building reusable ML components and improving end-to-end ML pipelines.

Why you will love this role
  • Opportunity to work on impactful ML solutions with real-world business applications.
  • Exposure to a wide range of machine learning, deep learning, and MLOps technologies.
  • Collaborative environment where you can influence model design, deployment strategy, and ML architecture.
  • Chance to work across the full ML lifecycle — from experimentation to production.
  • A culture that encourages continuous learning, innovation, and the adoption of modern ML tools and practices.

We would like you to bring along
  • Strong proficiency in Python for machine learning and data engineering tasks.
  • Hands-on experience with core ML libraries such as scikit-learn, XGBoost, and LightGBM.
  • Experience working with deep learning frameworks like TensorFlow, PyTorch, or Keras.
  • Solid understanding of data handling tools — NumPy and pandas.
  • Ability to create visualizations using Matplotlib, Seaborn, or Plotly.
  • Experience in Natural Language Processing (NLP), including text preprocessing, embeddings, and transformer-based techniques.
  • Familiarity with MLOps tools such as MLflow, Docker, or cloud-based ML services (AWS SageMaker, Azure ML, GCP Vertex).

Good-to-have skills
  • Knowledge of React or Angular for model visualization or UI integration.
  • Experience working with public cloud platforms (AWS, Azure, or GCP).
  • Understanding of CI/CD concepts and automated ML pipelines.

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