Principal Technical Architect

GlobalLogic; Dadri; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

GlobalLogic in Noida/Bangalore area is seeking a seasoned Principal/Staff AI Architect to define the technical vision for production-grade AI and GenAI systems. You will shape architecture patterns, blueprints, and MLOps practices to scale AI across the organization.

With 15+ years in software/data/ML and 3–4 years in an AI architect role, you will lead end-to-end design of AI/ML solutions, RAG pipelines, and model governance, while collaborating with cross-functional teams to ensure secure,

Qualifications

• 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 delivering production ML systems, not just prototyping.
• Strong hands-on or design-level GenAI expertise: LLMs, embeddings, vector search, agents.
• Familiarity with Azure OpenAI, AWS Bedrock, Google Vertex AI.
• Understanding of responsible AI: fairness, explainability, safety, governance.
• Ability to communicate architectural trade-offs clearly to technical and business stakeholders.

Responsibilities

• Design end-to-end AI/ML solution architectures from data ingestion to deployment and monitoring.
• Lead the architecture of Generative AI applications: RAG pipelines, fine-tuning, prompt frameworks, agents.
• Define MLOps practices and platforms: model registries, experiment tracking, CI/CD, observability.
• Evaluate AI frameworks, foundation models, vector databases, and orchestration tools.
• Collaborate with data, platform, and application teams to ensure scalable, secure, production-ready AI.
• Stay current with AI landscape and translate capabilities into architectural recommendations.
• Contribute to RFPs, client engagements, and internal knowledge-sharing on AI patterns.

Skills

AI Architect

GenAI expertise

ML systems design

Vector databases

LLMs

MLOps

Stakeholder comms

Education

Google Professional Machine Learning Engineer

Deep Learning Specialization (Coursera / deeplearning.ai)

TOGAF Foundation or Certified

Tools

LangChain

LlamaIndex

Semantic Kernel

Weaviate

Pinecone

Job description

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

• 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