AI/ML Developer - CDOT

Job Role : AI/ ML Developer - CDOT

No. of Vacancy : 5

Qualification : ME/ M.Tech/ B.E/ B.Tech (CSE/IT) (with minimum 60% throughout the academics)

Experience : 2-3 years of AI/ML/DL professional work experience post qualification.

Roles & Responsibilities :

• Model Development: Design, develop, and train machine learning models for various applications, including classification, regression, clustering, and anomaly detection.

• Data Analysis & Preparation: Data engineering and analysis to preprocess and analyze large datasets, ensuring high-quality data input for model training.

• Algorithm Optimization: Research and implement advanced machine learning algorithms and techniques to enhance model performance and scalability.

• Model Deployment: Deploy machine learning models into production environments, ensuring seamless integration and monitoring.

• Continuous Improvement: Monitor and evaluate model performance in production, applying necessary updates and improvements based on real-time data and feedback.

• Research & Innovation: Stay up-to-date with the latest advancements in machine learning, AI, and data science, and apply innovative solutions to solve complex problems.

• Collaboration: Work closely with cross-functional teams, including backend engineers, UI designers, academic institutions and other stakeholders, to understand requirements and deliver effective solutions.

• Documentation: Maintain thorough documentation of model architecture, processes, experiments, and findings for future reference and knowledge sharing.

Skills :

1. Technical Proficiency:

Machine Learning Frameworks: Advanced Proficiency in utilizing frameworks such as TensorFlow, PyTorch, ONNX, TensorRT, TFLite, Scikit-learn, Keras, Pandas, and XGBoost for developing, training, and optimizing machine learning models.

Programming Languages: Advanced proficiency in Python, and familiarity with Java, C++, for implementing algorithms and handling large-scale data processing.

Deep Learning: Strong understanding of neural networks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and autoencoders. Experience in designing and fine-tuning deep learning architectures for complex tasks.

Natural Language Processing (NLP): Experience with NLP tools and techniques, such as Transformers, BERT, GPT, LSTM, word embeddings, RAGs for tasks like sentiment analysis, text classification, and language modeling.

Computer Vision: Proficiency in applying computer vision techniques, including image classification, object detection, and facial recognition, using libraries like OpenCV, YOLO, Mask R-CNN, Vision Transformers

Model Evaluation & Tuning: Expertise in model evaluation techniques, including cross-validation, confusion matrices, ROC curves, and AUC, coupled with hyperparameter tuning for optimal model performance.

2.Mathematical & Statistical Knowledge:

Linear Algebra & Calculus: Strong foundation in linear algebra and calculus, essential for understanding and implementing machine learning algorithms.

Probability & Statistics: In-depth knowledge of statistical methods, hypothesis testing, probability theory, and Bayesian inference as applied to data analysis and model development.

Optimization Techniques: Familiarity with optimization algorithms like gradient descent, stochastic gradient descent (SGD), Adam, and RMSprop, essential for model training and fine-tuning.

3. Data Handling & Engineering:

Data Preprocessing: Expertise in data wrangling, cleaning, normalization, and feature engineering to prepare datasets for model training and evaluation.

Big Data Tools: Experience with distributed computing frameworks like Apache Hadoop, Spark, or Dask, enabling efficient processing of large datasets.

Database Management: Proficiency in SQL for querying relational databases and NoSQL databases like MongoDB or Cassandra for handling unstructured data.

4. Research & Innovation:

Continuous Learning: Eagerness to stay updated with the latest advancements in machine learning, artificial intelligence, and data science, and the ability to apply cutting-edge techniques to ongoing projects.

Publication & Conference Participation: Demonstrated ability to contribute to the academic and professional community through publications, attending conferences, and engaging in knowledge sharing activities.