Youverify is building trust in Africa by helping businesses and individuals confirm identity and physical addresses. Using artificial intelligence, Youverify confirms a user’s identity document and compares it with their facial biometrics. This information can be cross-checked against more than 300 databases locally and globally. Youverify in partnership with over 20,000 field verification officers verifies physical addresses all over Sub-Sahara Africa in less than 48 hours. We bridge the gap in Africa's digital industry by providing one API for identity and physical address verification.
About the job
- The ideal candidate will have a minimum of 5 years of experience in designing, developing, and implementing machine learning models and algorithms to solve complex problems and improve our products and services.
- You will collaborate with data scientists, software engineers, product managers, and domain experts to translate research findings into practical solutions, ensuring alignment with business goals to create scalable solutions that deliver actionable insights.
Key Responsibilities
Model Development:
- Design and develop cutting-edge machine learning models and algorithms tailored for diverse applications.
- Engage in rigorous model experimentation and validation to ensure high-performance outcomes.
Data Processing:
- Collect, preprocess, and analyze large-scale datasets to train and validate machine learning models.
- Employ advanced data wrangling techniques to enhance data quality and model accuracy.
Deployment:
- Implement and integrate machine learning models into production environments, focusing on scalability, robustness, and performance.
- Collaborate on deployment pipelines to streamline the transition from development to production.
Optimization:
- Continuously monitor and enhance the performance of deployed models through iterative refinement and hyperparameter tuning.
- Utilize feedback loops and performance metrics to drive model improvements.
Collaboration:
- Partner closely with cross-functional teams, including data scientists, software engineers, and business analysts, to gather requirements and deliver comprehensive solutions.
- Elevate the team's expertise by sharing best practices, conducting workshops, and fostering a culture of continuous learning and innovation.
Research:
- Stay abreast of the latest advancements in machine learning and AI, and proactively incorporate new techniques and tools into ongoing projects.
- Conduct independent research to explore emerging trends and technologies.
Documentation:
- Document processes, models, and methodologies meticulously to ensure reproducibility and facilitate knowledge sharing across the organization.
- Maintain clear and comprehensive documentation to support ongoing project development and future scalability.
Qualifications
Education:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, Machine Learning or a related field.
Experience:
- 5+ years experience in delivering and operationalising Machine learning models in production preferably in a startup environment
- At least 1 year of leadership experience managing a minimum of 2 persons.
- Extensive experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Proficiency in programming languages such as Python, R, Rust or Java.
- Experience with big data technologies (e.g., Hadoop, Spark)
- Familiarity with MLOps practices
- Proven experience using Azure and Terraform to build, deploy, and manage high-quality ML models
- Deep understanding of machine learning algorithms and principles.
- Have constructed batch and streaming microservices exposed as REST/gRPC endpoints
- Advanced experience with data processing and analysis tools (e.g., Pandas, NumPy).
- Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) and ML services.
- Knowledge of database systems (SQL and NoSQL).
- Excellent problem-solving skills and attention to detail.
- Strong communication and teamwork abilities.
- Highly skilled with distributed processing architecture and ML/data workflow management platforms (Spark, Databricks, Airflow, Kubeflow, MLflow etc)
- Knowledge of deep learning techniques and frameworks
- Highly skilled with strong proficiency in MLOPS with experience in building large-scale ML applications, services, pipelines and architecture
- Experience with containerization such as Docker and Kubernetes
- Experience with CI/CD pipelines, integrated tests and test-driven development
Method of Application
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