Principal Technical Architect

AI Architect

Location: [Noida// Bangalore/ Pune/ Nagpur/ Chennai] | Level: Principal / Staff | Experience: 15+ years (3–4 years in an Architect role)

About the Role

This is a newly created position for an AI-first thinker who can architect production-grade AI and Generative AI systems. You will be responsible for defining the technical vision, design patterns, and implementation blueprints for ML, AI, and GenAI solutions across the organisation.

Key Responsibilities

• Design end-to-end AI/ML solution architectures — from data ingestion and model development through to deployment, monitoring, and feedback loops
• Lead the architecture of Generative AI applications: RAG pipelines, LLM fine-tuning strategies, prompt engineering frameworks, and agent-based systems
• Define MLOps practices and platforms: model registries, experiment tracking, CI/CD for ML, model observability
• Evaluate and recommend AI frameworks, foundation models, vector databases, and orchestration tools (e.g. LangChain, LlamaIndex, Semantic Kernel, Weaviate, Pinecone)
• Collaborate with data, platform, and application teams to ensure AI solutions are scalable, secure, and production-ready
• Stay current with the rapidly evolving AI landscape and translate emerging capabilities into actionable architectural recommendations
• Contribute to RFPs, client engagements, and internal knowledge-sharing on AI architecture patterns

Required Skills & Experience

• 15+ years in software engineering, data science, or ML engineering
• 3–4 years in an AI/ML Architect or equivalent senior design role
• Proven experience designing and delivering production ML systems — not just prototyping
• Strong hands-on or design-level expertise in GenAI: LLMs, multimodal models, embedding models, vector search, and agentic frameworks
• Familiarity with major cloud AI services: Azure OpenAI, AWS Bedrock, Google Vertex AI
• Understanding of responsible AI principles: fairness, explainability, safety, and governance
• Ability to communicate architectural trade-offs clearly to both technical teams and business stakeholders

Preferred Certifications

• Microsoft Azure AI Engineer Associate (AI-102)
• AWS Certified Machine Learning – Specialty
• Google Professional Machine Learning Engineer
• Deep Learning Specialization (Coursera / deeplearning.ai) — widely recognised in the field
• TOGAF (Foundation or Certified) — beneficial for enterprise-level AI architecture engagements