AI Architect

r3 Consultant

Dadri

On-site

INR 2,000,000 - 3,000,000

Full time

14 days+

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Job summary

A leading consultancy firm in Uttar Pradesh seeks an AI Architect to design and implement enterprise-scale AI/ML solutions using the Microsoft Azure platform. The role involves collaborating with data scientists, architecting hybrid AI solutions, and ensuring responsible AI governance. Candidates should have over 7 years of experience in software engineering or AI/ML roles, with deep expertise in Azure. This position offers a chance to contribute to innovative Insurtech solutions while driving measurable business outcomes.

Qualifications

Experience with Microsoft AI Technology Stack is essential.
Proficiency in AI/ML frameworks and model development required.
Experience in implementing AI governance frameworks in regulated industries.

Responsibilities

Design and implement enterprise-scale AI/ML solutions.
Collaborate with data scientists to optimize AI capabilities.
Lead AI governance initiatives for responsible AI practices.

Skills

Deep expertise in Azure OpenAI Service

Experience with Google Cloud AI Platform

Proficiency with large language models

Hands-on experience with n8n workflow orchestration

Understanding of AI applications across insurance

Expertise in explainability tools

Education

Seven or more years in software engineering or AI/ML

Tools

Azure Machine Learning

Python

TensorFlow

Job description

Job Summary

Join us as an AI Architect at r3 Consultant. Build modern Insurtech AI‑underpinned solutions that unlock value for internal and external customers. In this role you will design and implement artificial intelligence and machine learning solutions across the Xceedance ecosystem, combining deep expertise in AI/ML technologies with solution architecture. Your work will deliver intelligent, scalable, and responsible AI systems that drive measurable business outcomes.

Key Responsibilities

• Design and implement enterprise‑scale AI/ML solutions using Microsoft Azure AI platform, while also incorporating Google AI capabilities when strategically beneficial. • Work closely with data scientists to bring architectural rigor and best practices to existing AI capabilities. • Architect hybrid AI solutions that combine public large language models (GPT‑4, Claude, Gemini) with custom‑trained small language models fine‑tuned on proprietary insurance data for underwriting automation, claims processing, policy administration, and fraud detection. • Define the AI strategy and roadmap, evaluate and select appropriate frameworks, platforms, and tools, and design n8n workflow orchestration and AI model lifecycle management processes. • Collaborate with AI teams to optimize model training pipelines, implement few‑shot and zero‑shot learning architectures, and ensure cost‑effective deployment of domain‑adapted models. • Lead AI governance initiatives by designing responsible AI frameworks that ensure fairness, transparency, and explainability, and comply with GDPR, the EU AI Act, Solvency II, and other insurance regulations. • Drive continuous improvement of AI systems through innovation, experimentation, and knowledge sharing. Mentor team members on emerging AI technologies and industry best practices.

Required Skills

Microsoft AI Technology Stack – Deep expertise in Azure OpenAI Service (GPT‑4, embeddings, document processing), Azure Machine Learning (AutoML, MLOps, model registry, managed endpoints), Azure AI Services (Document Intelligence, Cognitive Search), Azure AI Studio (prompt flow, evaluation tools), Azure Databricks (MLflow integration), Azure Synapse Analytics, and Microsoft Fabric.
Multi‑Platform AI Capabilities – Experience with Google Cloud AI Platform including Vertex AI, Gemini multimodal capabilities, and BigQuery ML, balanced with primary Microsoft stack expertise.
AI/ML Frameworks & Model Development – Proficiency with large language models (GPT‑4, Claude, Gemini, LLaMA) 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 (Transformers, PEFT, quantization, pruning, knowledge distillation).
Orchestration & Automation – Hands‑on experience with n8n workflow orchestration, 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: underwriting automation, claims processing, document processing (OCR, contract analysis), customer service automation, actuarial analytics, and regulatory compliance automation.
Responsible AI & Governance – Expertise in explainability tools (SHAP, LIME, attention visualization), bias detection frameworks (Fairlearn, AI Fairness 360), model risk management, and regulatory compliance frameworks, with a deep understanding of AI ethics principles and privacy‑preserving techniques.

Relevant Experience

• Seven or more years in software engineering, data science, or AI/ML roles with a strong emphasis on the Microsoft Azure ecosystem. • At least three years of architecting and deploying production AI/ML systems at scale, preferably within insurance or financial services. • Demonstrated track record of training and fine‑tuning small language models for domain‑specific business processes. • Hands‑on experience implementing n8n workflow orchestration, establishing AI governance frameworks in regulated industries, and working with both public LLMs and private custom model deployments. • Proven success in designing hybrid architectures that combine large and small models for cost‑effective, high‑performance systems, and evidence of improving existing AI systems through architectural innovation.

Key Competencies

Insurance Domain Knowledge – Understanding of insurance business processes, data structures, regulatory requirements, actuarial concepts, and risk modeling principles.
Technical Leadership & Collaboration – Ability to mentor data scientists and ML engineers, 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, evaluating emerging technologies, driving proof‑of‑concepts for innovative insurance AI applications, and fostering a culture of experimentation.

Certifications