
AI ML Engineer
2 days ago
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:
- 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.
- Machine Learning Algorithms: Should have a deep understanding of supervised learning (linear regression, decision trees, SVM), unsupervised learning, and reinforcement learning.
- Data Management and Analysis: Skills in data cleaning, feature engineering, and data transformation are crucial.
- Deep Learning: Familiarity with neural networks, CNNs, RNNs, and other architectures is important.
- Machine Learning Frameworks and Libraries: Experience with TensorFlow, PyTorch, Keras, or Scikit-learn is valuable.
- Natural Language Processing (NLP): Familiarity with NLP techniques like word2vec, sentiment analysis, and summarization can be beneficial.
- Cloud Computing: Experience with cloud-based services like AWS SageMaker, Google Cloud AI Platform, or Microsoft Azure Machine Learning.
- Data Preprocessing: Skills in handling missing data, data normalization, feature scaling, and data transformation.
- Feature Engineering: Ability to create new features from existing data to improve model performance.
- Data Visualization: Familiarity with visualization tools like Matplotlib, Seaborn, Plotly, or Tableau.
- Containerization: Knowledge of containerization tools like Docker and Kubernetes.
- Databases: Understanding of relational databases (e.g., MySQL) and NoSQL databases (e.g., MongoDB).
- Data Warehousing: Familiarity with data warehousing concepts and tools like Amazon Redshift or Google BigQuery.
- Computer Vision: Understanding of computer vision concepts and techniques like object detection, segmentation, and image classification.
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