Senior AI Architect

Job Description: Principal AI/ML Architect GenAI & Agentic AI

Role Overview

We are looking for a Principal AI/ML Architect with deep expertise in Traditional Machine Learning, Generative AI, and Agentic AI architectures to lead the design and delivery of enterprise-scale AI solutions. This role requires strong hands-on capability along with architectural ownership, stakeholder engagement, and technical leadership across the AI lifecycle.

Experience

• 14 to 24 years of overall IT experience
• 10+ years of hands-on experience in Data Science / AI / ML
• Prior experience in Architect / Principal / Lead roles is mandatory

Key Responsibilities

• Architect, design, and lead end-to-end AI/ML and GenAI solutions from ideation to production
• Define enterprise-grade architectures for:
• Traditional ML & Deep Learning systems
• Generative AI (LLMs) use cases
• RAG, Self-RAG, and advanced retrieval pipelines
• Agentic AI / Multi-Agent orchestration frameworks
• Own solution design covering:
• Data ingestion, feature engineering, model training
• Deployment, monitoring, retraining, and drift detection
• Design scalable AI services using microservices and event-driven architectures
• Lead MLOps pipelines for CI/CD, model versioning, monitoring, and governance
• Work closely with business stakeholders to translate requirements into AI-driven solutions
• Mentor and guide teams of data scientists, ML engineers, and MLOps engineers
• Drive POCs, innovation initiatives, and adoption of state-of-the-art AI techniques
• Ensure solutions meet performance, security, compliance, and explainability standards

Technical Skills Mandatory

AI / ML / GenAI

• Machine Learning, Deep Learning, NLP, Computer Vision
• Generative AI using GPT, LLaMA, Mistral, Claude or equivalent LLMs
• RAG, Self-RAG, embeddings, vector databases
• Agentic AI / Multi-Agent frameworks (LangChain, LangGraph, AutoGen, CrewAI or similar)
• Statistical modeling, feature engineering, model evaluation

Programming & Frameworks

• Python (strong hands-on)
• TensorFlow / PyTorch / Keras
• REST APIs & microservices

Cloud & MLOps

• AWS / GCP / Azure (any one mandatory, multi-cloud preferred)
• Docker, Kubernetes
• CI/CD pipelines, model monitoring, logging, drift detection
• Tools like SageMaker, Bedrock, Airflow, MLflow (or equivalents)

Data & Platforms

• SQL & NoSQL databases
• Vector databases & knowledge graphs
• Kafka / SQS or similar event-driven systems

Nice to Have

• Experience in Banking, Financial Services, Investment Banking, Healthcare, or Automation
• Experience handling large-scale enterprise AI deployments
• Exposure to Explainable AI, Responsible AI, AI governance
• Strong understanding of Agile delivery models

Soft Skills

• Strong communication and stakeholder management
• Ability to translate business problems into AI solutions
• Technical leadership and mentoring capability
• Strategic thinking with hands-on execution mindset

Role Level

• Principal / Architect / Senior Architect AI & GenAI
• Individual contributor with technical ownership and leadership responsibilities