Model Development: Design, build, and train machine learning models, including supervised, unsupervised, and reinforcement learning algorithms.
Data Preparation: Collect, preprocess, and analyze large datasets to ensure high-quality input for machine learning systems.
Model Optimization: Fine-tune models for performance, scalability, and efficiency using techniques like hyperparameter optimization and feature engineering.
Production Deployment: Implement and deploy machine learning models into production environments, ensuring seamless integration with existing systems.
Monitoring and Maintenance: Continuously monitor model performance in production and implement retraining or updates as needed.
Collaboration: Partner with cross-functional teams, including data scientists, product managers, and software engineers, to deliver end-to-end solutions.
Innovation: Stay up-to-date with the latest trends and advancements in machine learning, artificial intelligence, and related technologies to drive innovation.

Required Qualifications
Education: Bachelor s or Master s degree in Computer Science, Data Science, Artificial Intelligence, or a related field (PhD is a plus).
Experience




Proven experience in designing and implementing machine learning models in real-world applications.
Strong understanding of machine learning algorithms, frameworks, and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Programming Skills: Proficiency in Python, R, or Java, with experience in data analysis and modeling libraries (e.g., NumPy, Pandas, Matplotlib).
Mathematics and Statistics: Strong foundation in linear algebra, calculus, probability, and statistics.
Cloud and Big Data: Experience with cloud platforms (AWS, Google Cloud, Azure) and big data tools (e.g., Hadoop, Spark).
Version Control: Familiarity with version control systems like Git.

Preferred Qualifications
Experience with NLP, computer vision, or deep learning.
Familiarity with MLOps practices, including CI/CD pipelines for ML.
Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).

Key Competencies
Robust analytical and problem-solving skills.
Excellent communication and collaboration abilities.
Eagerness to learn and adapt in a rapid-paced workplace.
Ability to translate business problems into machine learning solutions.

📌 Machine Learning Engineer New Delhi (India)
🏢 Digital Global Services
📍 India