AI and ML engineer

Job description - ML Engineer About Us: Join a fintech company, where we harness the power of machine learning and artificial intelligence to redefine financial services and insurance solutions. Our team is dedicated to delivering cutting-edge data-driven solutions that transform customer experiences and streamline operations. We are looking for passionate ML Engineers with 2-4 years of experience to join our dynamic team and drive innovation in this rapidly evolving sector. Responsibilities: 1. Collaborate with Cross-Functional Teams: Work closely with product managers, data scientists, software engineers, and business stakeholders to design, develop, and implement machine learning models that address key challenges in the fintech and insurance space. 2. Analyze and Interpret Complex Data: Engage in exploratory data analysis and build robust models by analyzing large and diverse datasets, including customer behavior, transaction records, and risk assessment data, to extract actionable insights. 3. Design and Maintain ML Pipelines: Develop, optimize, and maintain end-to-end machine learning pipelines, encompassing data preprocessing, feature extraction, model training, and evaluation, ensuring efficiency and scalability. 4. Model Deployment and Monitoring: Deploy models into production environments, ensuring seamless integration with existing systems. Monitor model performance and retrain models as needed to maintain accuracy and reliability over time. 5. Innovate with Industry Trends: Stay up-to-date with the latest advancements in machine learning, artificial intelligence, and fintech-insurance applications. Leverage new techniques, frameworks, and tools to enhance model performance and address emerging business needs. 6. Establish Best Practices: Contribute to the development and documentation of best practices, standards, and guidelines for machine learning engineering within the organization, fostering a culture of continuous improvement. Desired Skills & Qualifications: 1. 2-4 years of hands-on experience in designing, building, and deploying machine learning models in production environments, with a proven track record of implementing live projects. 2. Strong understanding of both supervised and unsupervised learning techniques, including but not limited to regression, classification, clustering, dimensionality reduction, and anomaly detection. 3. Proficiency in Python and relevant ML libraries (e.g., PyTorch, Scikit-Learn, Pandas). 4. Ability to create insightful visualizations using tools like Matplotlib, Seaborn, or Plotly. 5. Experience with big data processing frameworks such as Spark, Hadoop, or similar, and familiarity with cloud platforms like AWS, GCP, or Azure for deploying ML models. 6. Experience with databases (SQL and NoSQL) and data storage solutions such as PostgreSQL, MongoDB, or AWS RDS. 7. Experience with ETL processes, data preprocessing, and feature engineering. 8. Strong analytical and problem-solving skills, with the ability to interpret complex data and present insights clearly to technical and non-technical stakeholders. 9. Experience in developing and optimizing algorithms for real-world applications, especially in the context of financial services, risk analysis, fraud detection, and customer personalization. 10.Experience in deploying and monitoring ML models in live production environments using tools like Docker, Kubernetes, or cloud-based services (e.g., AWS SageMaker, Google AI Platform). Experience with A/B testing and model retraining pipelines is a plus. 11.Excellent communication skills, with the ability to work collaboratively in a fast-paced, team-oriented environment. What We Offer: 1. Opportunity to work on impactful projects that directly shape the future of fintech and insurance. 2. A collaborative and innovative work environment that encourages learning and growth. 3. Competitive salary, benefits, and the chance to work with cutting-edge technologies in a rapidly growing industry. Role & responsibilities

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