Astra Capital Group LLC
Astra Capital Group LLC is a US-registered firm committed to providing both new and existing companies with top-notch technical services. Our specialty is offering creative, scalable solutions that are tailored to each client’s particular requirements. With an emphasis on promoting efficiency and growth, our services are affordable each month, making them available to businesses of all kinds. Whether you want to use cutting-edge technologies, optimize processes, or improve your IT infrastructure, Astra Capital Group is dedicated to providing outstanding value and outcomes.
We are recruiting to fill the position below:
Job Title: Senior Machine Learning Engineer (Remote)
Locations: Abuja (FCT), Lagos, Abia, Adamawa and Akwa Ibom
Employment Type: Full-time
Overview of the Position
To join our remote team, we are looking for a Senior Machine Learning Engineer with at least 4 years of experience who is highly talented.
Designing, creating, and implementing machine learning models that drive creative solutions in a range of business sectors will be your responsibility in this position.
In order to deploy end-to-end machine learning systems that streamline workflows, improve decision-making, and enhance the user experience generally, you will work with cross-functional teams.
Principal Duties
1.Development of Machine Learning Models:
- Create, construct, and train machine learning models for anomaly detection, recommendation systems, predictive
analytics, and other applications.
- Use cutting-edge algorithms, such as reinforcement learning, deep learning, and supervised and unsupervised learning.
- To enhance model performance, work with huge datasets and utilize feature engineering, data augmentation, and suitable preprocessing technique
2.Complete ML Systems:
- Create and implement complete machine learning systems, including model creation and production environment integration.
- Work together with software engineers to guarantee that models are seamlessly incorporated into current applications and systems.
- Use retraining techniques and ongoing monitoring to make sure models stay accurate and applicable over time.
Model Scaling and Optimization: - Make that machine learning models satisfy production standards by optimizing them for efficiency, scalability, and performance.
- To improve model efficiency, use strategies like parallelization, model compression, and hyperparameter optimization.
- Use distributed systems and cloud computing re
3.Cooperation and Information Exchange:
- Collaborate closely with engineers, product managers, and data scientists to comprehend business goals and convert them into machine learning solutions.
- Assist in improving the team’s proficiency in AI and machine learning by mentoring and advising junior team members.
- Take part in code reviews and make sure the team uses high-quality, maintainable code practices.
4. Innovation and Research;
- Keep up with the most recent developments in data science, AI, and machine learning, and use fresh methods to improve model performance.
- Participate in research projects, try out novel algorithms, and promote ongoing enhancements to the machine learning process.
- Use internal documents, workshops, or presentations to impart information to the larger team.
Qualifications
- Minimum of 4 years of professional experience in machine learning engineering or data science.
- Strong proficiency in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Keras).
- Solid understanding of machine learning algorithms, statistical methods, and model evaluation metrics.
- Experience working with big data technologies and cloud platforms (e.g., AWS, GCP, Azure) for deploying and managing ML models.
- Strong knowledge of data preprocessing, feature engineering, and model selection.
- Experience in designing, developing, and deploying machine learning models in production environments.
- Proficiency in programming languages such as Python, R, or Java.
Preferred Skills:
- Experience with deep learning techniques (e.g., CNNs, RNNs, LSTMs, transformers).
- Knowledge of MLOps practices, including model versioning, CI/CD for machine learning, and model monitoring.
- Experience with automated machine learning tools and platforms (e.g., H2O.ai, AutoML).
- Familiarity with NLP, computer vision, or reinforcement learning applications.
- Experience with containerization tools like Docker and orchestration tools like Kubernetes for model deployment.
What We Offer
- Competitive salary and comprehensive benefits package.
- Opportunities for career growth and professional development in machine learning and AI.
- A fully remote, flexible, and collaborative work environment.
- The chance to work on cutting-edge projects that shape the future of our industry.
How to Apply
Interested and qualified candidates should send their Resume, Portfolio, and any relevant machine learning projects to: hiring@kavsis.com using the Job Title as the subject of the mail
Application Deadline 20th January, 2025.