AI Architect

Job Title: AI Architect

Work Experience: 6-10 years

Location- Noida Sector 62

Role Summary

Work with us to build modern Insurtech AI underpinned solutions we are a growing team of hands on architects striving to build high quality solutions for our internal and external customers. The AI Architect is responsible for designing and implementing artificial intelligence and machine learning solutions across the Xceedance ecosystem. This role combines deep expertise in AI/ML technologies with solution architecture to deliver intelligent scalable and responsible AI systems that drive business value.

Key Responsibilities

The AI Architect designs and implements enterprise-scale artificial intelligence and machine learning solutions across the Xceedance insurance ecosystem leveraging the Microsoft Azure AI platform as the primary technology stack while maintaining flexibility to incorporate Google AI capabilities where strategically advantageous. This role combines deep technical expertise in AI/ML with solution architecture to deliver intelligent scalable and responsible AI systems that drive measurable business value.

Working extensively with existing AI teams including data scientists the architect brings architectural rigor and best practices to enhance and scale current AI capabilities. The role focuses on architecting hybrid AI solutions that combine public large language models (GPT-4 Claude Gemini) for general-purpose reasoning with custom-trained small language models fine-tuned on proprietary insurance data for domain-specific business processes including underwriting automation claims processing policy administration and fraud detection.

The architect helps define the AI strategy and roadmap aligned with insurance industry business objectives evaluating and selecting appropriate frameworks platforms and tools. Key responsibilities include designing n8n workflow orchestration establishing AI Model lifecycle management processes and architecting data platforms supporting AI workloads through Azure Synapse Analytics Databricks and feature stores.

Working to training and fine-tuning small language models on insurance-specific datasets forms a critical component requiring the design of efficient data collection and annotation strategies model compression techniques and transfer learning approaches. The architect collaborates closely with existing AI expertise to optimize model training pipelines implement few-shot and zero-shot learning architectures and ensure cost-effective deployment of domain-adapted models suitable for high-volume insurance operations.

Leading AI governance initiatives the architect designs responsible AI frameworks ensuring fairness transparency and explainability in insurance AI systems critical for regulatory compliance with GDPR the EU AI Act and insurance-specific regulations including Solvency II. This includes establishing AI ethics policies implementing model risk management frameworks creating comprehensive audit trails and defining standards for bias detection mitigation and model explainability across all AI/ML solutions.

The role drives continuous improvement of AI systems through innovation and experimentation identifying opportunities to enhance organizational capabilities including AI-powered development processes automated testing and intelligent quality analysis. Fostering a culture of knowledge sharing and technical excellence the architect mentors team members on emerging AI technologies architectural patterns and industry best practices.

Required Skills

Microsoft AI Technology Stack - Deep expertise in Azure OpenAI Service (GPT-4 embeddings document processing) Azure Machine Learning (Automated ML MLOps model registry managed endpoints) Azure AI Services (Document Intelligence Cognitive Search) Azure AI Studio (prompt flow evaluation tools) Azure Databricks (unified analytics MLflow integration) Azure Synapse Analytics and Microsoft Fabric for integrated data science workloads.

Multi-Platform AI Capabilities - Experience with Google Cloud AI Platform including Vertex AI Gemini multi-modal capabilities and BigQuery ML for in-database machine learning on large insurance datasets balanced with primary Microsoft stack expertise.

AI/ML Frameworks & Model Development - Proficiency in working with large language models (GPT-4 Claude Gemini LLaMA) alongside training and fine-tuning small domain-specific models using BERT DistilBERT and RoBERTa. Strong command of PyTorch TensorFlow scikit-learn XGBoost and the Hugging Face ecosystem including Transformers PEFT for parameter-efficient fine-tuning and model compression techniques including quantization pruning and knowledge distillation.

Orchestration & Automation - Hands-on experience with n8n workflow orchestration for AI pipeline automation and integration with Azure services complemented by knowledge of Azure Logic Apps Power Automate MLflow for experiment tracking and Azure DevOps or GitHub Actions for CI/CD pipelines supporting ML operations.

Insurance Domain AI Applications - Practical understanding of AI applications across the insurance value chain including underwriting automation (risk assessment pricing optimization) claims processing (triage fraud detection damage assessment) document processing (OCR contract analysis regulatory document understanding) customer service automation actuarial analytics (loss prediction reserves estimation) and regulatory compliance automation.

Responsible AI & Governance - Expertise in AI explainability tools (SHAP LIME attention visualization) bias detection frameworks (Fairlearn AI Fairness 360) model risk management and regulatory compliance frameworks. Deep understanding of AI ethics principles including fairness accountability transparency and privacy-preserving machine learning techniques.

Required Experience

Seven or more years in software engineering data science or AI/ML roles with demonstrable emphasis on the Microsoft Azure ecosystem including at least three years architecting and deploying production AI/ML systems at scale preferably within insurance or financial services environments. Proven track record of training and fine-tuning small language models for domain-specific business processes with strong collaborative experience working alongside AI/ML teams data scientists and ML engineers.

Hands-on experience implementing n8n workflow orchestration for AI pipelines establishing AI governance frameworks in regulated industries and working with both public LLMs and private custom model deployments. Demonstrated success building insurance-specific AI solutions and designing hybrid architectures that combine large and small models for cost-effective high-performance systems. Evidence of improving existing AI systems and processes through architectural innovation and best practices.

Key Competencies

Insurance Domain Knowledge - Understanding of insurance business processes data structures regulatory requirements actuarial concepts and risk modeling principles specific to the insurance industry.

Technical Leadership & Collaboration - Demonstrated ability to work effectively with existing AI experts and teams mentor data scientists and ML engineers on architectural best practices lead technical discussions and design reviews and bridge the gap between AI research and production implementation.

Innovation & Continuous Improvement - Commitment to staying current with AI/ML research and industry trends evaluating emerging technologies for organizational adoption driving proof-of-concepts for innovative insurance AI applications and fostering a culture of experimentation and continuous learning.

Certifications

Microsoft Certified: Azure AI Engineer Associate (AI-102) Microsoft Certified: Azure Data Scientist Associate (DP-100) and Microsoft Certified: Azure Solutions Architect Expert (AZ-305). Additional valuable certifications include Google Professional Machine Learning Engineer.

Required Skills:

azure ai LLM AI Architecture Open ai MLops