Machine Learning Engineer
3 weeks ago
At Unreal Gigs, we're on a mission to harness the potential of machine learning to drive innovation and growth. As a Machine Learning Engineer, you'll play a critical role in designing, implementing, and optimizing intelligent systems that unlock new opportunities for our clients.
Key Responsibilities:
Design and Develop Machine Learning Models:
Develop and deploy machine learning models using frameworks such as TensorFlow, PyTorch, or Scikit-learn. You'll work on applications like recommendation systems, predictive analytics, and natural language processing (NLP).
Data Collection, Cleaning, and Preprocessing:
Collaborate with data engineers and analysts to collect, clean, and prepare large datasets for training and evaluation. You'll design data pipelines that automate the preprocessing steps, ensuring the data is ready for model training.
Model Training and Hyperparameter Tuning:
Train machine learning models and perform hyperparameter optimization to improve model performance. You'll experiment with different algorithms, evaluate model accuracy, and optimize results based on business needs.
Deployment and Integration:
Work closely with software engineers to deploy machine learning models into production environments. You'll build APIs, integrate models into existing systems, and ensure models are scalable, maintainable, and easily accessible by applications.
Model Monitoring and Maintenance:
Continuously monitor the performance of deployed models and retrain or fine-tune them as needed to maintain accuracy and relevance. You'll establish systems to detect model drift and ensure that models adapt to new data over time.
Collaboration and Cross-Functional Support:
Partner with data scientists, software engineers, and product managers to identify opportunities for machine learning solutions that drive business objectives. You'll translate business problems into technical solutions, working across departments to deliver impactful results.
Stay Up-to-Date with AI Trends:
Stay current with the latest advancements in machine learning and AI technologies. You'll explore new techniques and tools, bringing innovative ideas to the team to enhance the capabilities of our machine learning solutions.
Requirements:
Machine Learning Expertise:
Strong knowledge of machine learning algorithms such as regression, classification, clustering, deep learning, and reinforcement learning. You're proficient with frameworks like TensorFlow, PyTorch, and Scikit-learn.
Programming Skills:
Proficiency in programming languages such as Python, R, or Java. You have hands-on experience with data manipulation libraries like Pandas, NumPy, or Spark and can write clean, efficient code.
Data Engineering:
Experience in handling large datasets, feature engineering, and data preprocessing. You know how to clean, normalize, and transform raw data to make it suitable for model training.
Model Deployment:
Familiarity with deploying machine learning models into production environments using cloud platforms (AWS, GCP, Azure) and containerization tools like Docker or Kubernetes.
Performance Optimization:
Strong ability to fine-tune models for performance and optimize algorithms for scalability and speed, ensuring models are robust in production environments.
Educational Requirements:
Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field. Equivalent experience in machine learning engineering is also highly valued. Certifications or advanced coursework in AI, machine learning, or data science are a plus.
Experience Requirements:
3+ years of experience in machine learning engineering, with a focus on building and deploying models in production environments. You've successfully developed solutions that solve real-world problems. Proven experience working with large datasets, designing machine learning pipelines, and optimizing model performance. Experience with cloud-based machine learning services (e.g., AWS SageMaker, Google AI Platform, Azure Machine Learning) is highly desirable.
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