Data Scientist

please share your resume at shobhana@futuristicuwltd.com Role: AI Engineer  Location: Noida-Hybrid Role: Design, develop, and deploy AI/ML solutions that transform insurance operations, enhance underwriting accuracy, and automate business workflows across our integrated ecosystem.  ey Responsibilities  AI Solution Development: Design and implement machine learning models for Property underwriting automation, claims triage, fraud detection, and risk assessment  Model Deployment & MLOps: Build and maintain ML pipelines, model versioning, A/B testing frameworks, and production monitoring systems  Data Engineering Integration: Collaborate with data teams to build feature stores, data pipelines, and real-time inference systems  GenAI Applications: Develop LLM-powered applications for document processing, customer service automation, and intelligent workflow orchestration  Insurance Domain AI: Build specialized models for policy analysis, claims processing, underwriting decision support, and regulatory compliance automation  Production Systems: Ensure AI systems meet enterprise standards for scalability, security, model governance, and regulatory compliance  Cross-Functional Collaboration: Work closely with business stakeholders, data scientists, and engineering teams to translate business requirements into AI solutions  Innovation & Research: Stay current with AI/ML advancements and evaluate emerging technologies for insurance applications  Code Quality & Documentation: Maintain high coding standards, comprehensive documentation, and knowledge sharing across the team  Performance Optimization: Monitor model performance, implement drift detection, and continuously improve AI system accuracy and efficiency  Required Qualifications  Education: B.Tech/M.Tech in Computer Science, Data Science, AI/ML, or related technical field  Experience: 3-8 years in AI/ML engineering with hands-on experience in production ML systems  Programming: Proficiency in Python, with experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn)  MLOps: Experience with ML pipeline tools (MLflow, Kubeflow, Airflow), containerization (Docker, Kubernetes), and cloud ML services  Data Processing: Strong skills in data manipulation (Pandas, NumPy), SQL, and working with large datasets  Cloud Platforms: Hands-on experience with AWS/Azure ML services, data lakes, and distributed computing  Model Development: Experience in supervised/unsupervised learning, deep learning, NLP, and computer vision  Production Deployment: Experience deploying ML models via APIs, batch processing, and real-time inference systems  Communication: Strong English communication skills for technical documentation and stakeholder interaction  Problem Solving: Analytical mindset with ability to translate business problems into AI/ML solutions  Preferred Qualifications  Insurance/Financial Services: Experience with insurance data, underwriting processes, claims workflows, or regulatory requirements  Generative AI: Hands-on experience with LLMs, RAG systems, prompt engineering, and AI application development  Advanced ML: Experience with ensemble methods, time series forecasting, anomaly detection, or recommendation systems  Big Data: Experience with Spark, Hadoop, or other distributed processing frameworks  Model Governance: Knowledge of model risk management, bias detection, explainable AI, and compliance frameworks  DevOps: Experience with CI/CD pipelines, infrastructure as code, and automated testing for ML systems  Research Background: Publications, patents, or contributions to open-source ML projects  Domain Expertise: Understanding of Property & Casualty insurance, underwriting, claims, or risk assessment  Startup Experience: Experience in fast-paced, high-growth environments with ability to work independently  Certifications: AWS/Azure ML certifications or relevant AI/ML professional certifications  Thanks; Shobhana