Established on the platform of e-Governance and smart technologies, Contec Global is amongst the most experienced independent technology and systems organizations operating in developing countries. Today, we are engaged globally in providing integrated, systems-based solutions in these fields. Our core focus is using technology to sustainably advance humanity. We are going beyond geographical and political borders in biometric technology, agriculture, green energy, transportation, and telecommunication. Our expertise in creating, developing, and implementing large-scale projects makes us confident that we can achieve uncommon results and rapid progress in sustainability in the coming years.
Job Summary
- As a Mid-Level AI/ML Engineer, you will design, develop, and deploy machine learning models to solve real-world problems in fraud detection, credit scoring, transaction monitoring, and customer personalization.
- You’ll work closely with data engineers, backend developers, and product teams to translate business needs into AI-driven solutions that scale.
Key Responsibilities
- Conduct exploratory data analysis, feature engineering, and statistical analysis to uncover patterns and improve model accuracy.
- Handle data cleaning, preprocessing, and managing imbalanced/complex Fintech datasets.
- Design and analyze experiments (e.g., A/B testing) to validate model business impact.
- Develop, train, and deploy ML models for fraud detection, risk assessment, credit scoring, and personalization.
- Collaborate with DevOps team to ensure high-quality, reliable data pipelines for ML workloads.
- Optimize models for performance, scalability, and low latency in production fintech environments.
- Implement explainable AI (XAI) techniques to ensure model transparency and compliance with financial regulations.
- Continuously monitor and improve deployed models with real-time data feedback loops.
- Contribute to the design of APIs for model inference and integration into core fintech applications.
- Stay updated with emerging AI/ML techniques, especially in the financial domain (graph ML, anomaly detection, NLP for KYC, etc.).
Requirements
- Education: Bachelor’s or Master's in Computer Science, Data Science, Engineering, or related field.
- Experience: 3–5 years of experience in AI/ML engineering, preferably in FinTech, banking, or payments.
Technical Skills:
- Deep knowledge of statistics, hypothesis testing, and classical ML algorithms.
- Strong programming skills in Python (Pandas, NumPy, Scikit-learn, PyTorch or TensorFlow).
- Proficiency in tools such as Jupyter Notebooks and in data visualization libraries e.g Matplotlib and Seaborn.
- Experience with SQL and working knowledge of big data tools (Spark, Dask, or similar).
- Hands-on experience deploying ML models via APIs, Docker, or cloud platforms (e.g AWS/GCP/Azure/Huawei).
- Familiarity with real-time systems (Kafka, NATS, or streaming pipelines) is a plus.
- Knowledge of fraud detection, anomaly detection, or credit risk models in fintech.
- Experience with process orchestration tools such as Prefect or Airflow is an advantage.
- Knowledge of regulatory compliance frameworks (e.g. PCI DSS, NDPR etc.).
Soft Skills:
- Strong problem-solving and analytical mindset.
- Ability to work in a fast-paced startup environment.
- Effective communicator and collaborator with cross-functional teams.
Nice to Have:
- Exposure to MLOps tools (MLflow, Kubeflow, or SageMaker).
- Knowledge of AI Orchestration framework (e.g. LangChain, Haystack).
- Experience with AI/Automation tools such as n8n and Zapier.
Method of Application
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