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