ML Engineer

Responsibilities:

• Design, develop, train, and deploy machine learning models for various business applications.
• Build and optimize predictive models using supervised, unsupervised, and deep learning techniques.
• Collect, clean, preprocess, and analyze large datasets to identify patterns and meaningful insights.
• Develop scalable data pipelines and feature engineering workflows to support ML model development.
• Implement machine learning algorithms using Python and relevant ML frameworks.
• Evaluate model performance using appropriate metrics and continuously improve accuracy, efficiency, and reliability.
• Deploy machine learning models into production environments using APIs, cloud platforms, and containerization tools.
• Collaborate with data scientists, backend engineers, and product teams to integrate ML solutions into existing applications.
• Implement model monitoring, versioning, retraining, and maintenance practices.
• Explore emerging machine learning technologies, frameworks, and methodologies to improve existing solutions.
• Ensure models are scalable, reliable, efficient, and aligned with business requirements.

Requirements:

• Bachelor's or master's degree in computer science, artificial intelligence, data science, engineering, or a related field.
• 2-6 years of hands-on experience in machine learning, AI engineering, or a related technical role.
• Demonstrated experience developing and deploying machine learning models.
• Strong understanding of statistical concepts, data structures, and machine learning fundamentals.
• Experience working with large datasets and building scalable ML pipelines.
• Ability to work independently and collaborate effectively with distributed teams.

Must Have:

• Strong programming experience in Python.
• Machine Learning Algorithms and Statistical Modelling.
• Supervised and Unsupervised Learning.
• Deep Learning and Neural Networks.
• Data Processing and Feature Engineering.
• NumPy, Pandas, Scikit-learn.
• TensorFlow or PyTorch.
• Model Training, Evaluation, and Optimization.
• SQL and Database Management.
• Data Structures and Algorithms.
• REST APIs and Model Deployment.
• Problem-Solving and Analytical Skills.

Good-to-Have Skills:

• Natural Language Processing (NLP).
• Generative AI and Large Language Models (LLMs).
• Retrieval-Augmented Generation (RAG).
• Hugging Face and Transformers.
• MLOps and ML Model Lifecycle Management.
• Docker and Kubernetes.
• AWS, Azure, or Google Cloud Platform (GCP).
• MLflow, Kubeflow, or similar experiment-tracking tools.
• CI/CD and Automated Deployment Pipelines.
• Computer Vision and Recommendation Systems.