AI ML Engineer

9 hours ago

Chennai, Tamil Nadu, India Nexxora Innovation and Tech Solutions Pvt. Ltd Full time ₹ 15,00,000 - ₹ 20,00,000 per year

Role: AI/ML Engineer

Location: Ambattur, Chennai(Onsite)

Fulltime Position

Job Summary:

We are looking for an AI/ML Engineer to Lead, develop, optimize, and deploy machine learning models for real-world applications. You will work on end-to-end ML pipelines, collaborate with cross-functional teams, and apply AI techniques such as NLP, Computer Vision, and Time-Series Forecasting. This role offers opportunities to work on cutting-edge AI solutions while growing your expertise in model deployment and optimization.

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.

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