AI/ML Lead Engineer

Role: AI/ML Lead Engineer

Location: Ambattur, Chennai(Onsite)

Fulltime Position

Job Summary:

AI/ML Engineer

Key Responsibilities:

• Design, build, and optimize

  machine learning models

   for various business applications.
• Develop and maintain

  ML pipelines

  , including data preprocessing, feature engineering, and model training.
• Work with

  TensorFlow, PyTorch, Scikit-learn, and Keras

   for model development.
• Deploy ML models in

  cloud environments (AWS, Azure, GCP)

   and work with

  Docker/Kubernetes

   for containerization.
• Perform

  model evaluation, hyperparameter tuning, and performance optimization

  .
• Collaborate with

  data scientists, engineers, and product teams

   to deliver AI-driven solutions.
• Stay up to date with the latest advancements in AI/ML and implement best practices.
• Write

  clean, scalable, and well-documented code

   in Python or R.

Technical Skills:

1. Programming Languages:

   Proficiency in languages like Python. Python is particularly popular for developing ML models and AI algorithms due to its simplicity and extensive libraries like NumPy, Pandas, and Scikit-learn.

2. Machine Learning Algorithms:

   Should have a deep understanding of supervised learning (linear regression, decision trees, SVM), unsupervised learning, and reinforcement learning.

3. Data Management and Analysis:

   Skills in data cleaning, feature engineering, and data transformation are crucial.

4. Deep Learning:

   Familiarity with neural networks, CNNs, RNNs, and other architectures is important.

5. Machine Learning Frameworks and Libraries:

    Experience with TensorFlow, PyTorch, Keras, or Scikit-learn is valuable.

6. Natural Language Processing (NLP):

    Familiarity with NLP techniques like word2vec, sentiment analysis, and summarization can be beneficial.

7. Cloud Computing:

   Experience with cloud-based services like AWS SageMaker, Google Cloud AI Platform, or Microsoft Azure Machine Learning.

8. Data Preprocessing:

   Skills in handling missing data, data normalization, feature scaling, and data transformation.

9. Feature Engineering:

   Ability to create new features from existing data to improve model performance.

10. Data Visualization:

    Familiarity with visualization tools like Matplotlib, Seaborn, Plotly, or Tableau.

11. Containerization:

     Knowledge of containerization tools like Docker and Kubernetes.

12. Databases

    : Understanding of relational databases (e.g., MySQL) and NoSQL databases (e.g., MongoDB).

13. Data Warehousing:

    Familiarity with data warehousing concepts and tools like Amazon Redshift or Google BigQuery.

14. Computer Vision:

     Understanding of computer vision concepts and techniques like object detection, segmentation, and image classification.