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At Airtime, we are committed to innovation to stay ahead. Each of us has a hunger for success and strives for excellence. Our culture is rooted in teamwork and shared humility, emphasizing collective achievement.
Our Approach
We keep our experience fresh through innovative, original features and continuous improvements. We aim for impactful change that sets new standards and differentiates us from competitors.
Our Values
Magnetic: Engaging and trustworthy, we connect easily, making every interaction memorable and visually appealing, including transforming data into personalized insights.
Uplifting: Bright and optimistic, we bring joy and transform routine interactions into moments of happiness.
The Opportunity
We are seeking an experienced AI/ML Tech Lead to join our growing team. You will lead the design and development of our internal systems, aligning with our ambitious AI roadmap.
Key Responsibilities
1. Lead the technical selection, design, and implementation of Airtime’s AI/ML strategy.
2. Design, build, and optimize NLP models for applications like search, analytics, chatbots, and sentiment analysis.
3. Develop and integrate personalization algorithms for recommendations and predictive analytics across customer touchpoints.
4. Implement MLOps best practices for scalable deployment in a cloud-first environment.
5. Collaborate with Data Scientists to manage and analyze structured and unstructured data to enhance customer experience.
6. Work with Data Engineers, Product, and Software teams to integrate ML solutions into fintech applications.
7. Research and implement state-of-the-art deep learning and NLP models (e.g., BERT, GPT).
8. Monitor model performance, ensure security, and retrain models as needed for real-time decision-making.
9. Promote the democratization of analytics through intuitive tools and APIs for non-technical teams.
10. Advocate for explainable AI (XAI) to ensure transparency and compliance.
11. Hire, mentor, and support junior ML engineers, fostering best practices.
Requirements
1. Strong background in Machine Learning, Deep Learning, and NLP.
2. Experience with transformer models (BERT, GPT, LLaMA, etc.).
3. Proficiency in SQL, Python, and ML libraries such as TensorFlow, PyTorch, Hugging Face, and Scikit-Learn.
4. Expertise in personalization techniques like recommendation systems and user segmentation.
5. Experience working with sensitive data and propensity models.
6. Hands-on experience deploying models in cloud environments (AWS & GCP).
7. Strong understanding of MLOps frameworks (MLFlow, ZenML, Kubeflow, Vertex AI, Sagemaker).
8. Experience mentoring junior engineers.
9. Experience with data visualization and analytics tools (Thoughtspot, Streamlit, Tableau, Power BI, etc.).
10. Share options and benefits outlined.
11. Additional benefits include 23+ days annual leave, birthday leave, flexible hours, life assurance, health plans, private medical insurance, hybrid working, and more.
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