Enterprise Architect

GlobalLogic; Dadri; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

GlobalLogic in India is seeking an Enterprise Architect to design and govern AI-driven architectures across data platforms, unstructured data pipelines, and embodied AI use cases. You will bridge business strategy and data architecture, define reference architectures, and guide integration with ERP and monitoring platforms while ensuring security and compliance.

The role requires 12–18+ years experience with 5+ years in AI/ML architecture, strong TOGAF/Zachman background, and hands-on exposure

Qualifications

• 12–18+ years overall EA experience with 5+ years in AI/ML architecture.
• Experience designing AI-driven architectures across data platforms and robotics.
• Knowledge of LLM ecosystems, vector stores, and RAG pipelines.
• Strong stakeholder management and CXO communication.

Responsibilities

• Define and own enterprise AI architecture strategy.
• Architect solutions unifying structured and unstructured data.
• Design agentic AI systems with multi-agent orchestration.
• Guide AI governance, data security, and sandboxing.
• Mentor teams on AI/ML best practices.
• Stay current with agentic frameworks and edge models.
• Translate business needs into scoped technical solutions.

Skills

Enterprise Architecture

TOGAF/Zachman

SQL/Data Warehousing

NLP/Computer Vision

Agentic AI

LLM Ecosystems

Cloud Architecture

AI Governance

Security & Compliance

Stakeholder Management

Pre-sales/SOW

Education

Bachelor's degree in Computer Science or related field

Tools

LangGraph

AutoGen

Pinecone

Weaviate

pgvector

ROS

SageMaker

Vertex AI

Job description

Location: Any GlobalLogic India office locations

Experience: 12–18+ years overall, with 5+ years in AI/ML architecture

Role Summary

We're looking for an Enterprise Architect who can design and govern AI-driven architectures across the full spectrum — from traditional structured data platforms to unstructured data pipelines, agentic AI systems, and emerging physical AI (robotics/embodied AI) use cases. This person will act as the technical bridge between business strategy, data architecture, and next-generation AI deployment, ensuring solutions are scalable, secure, and interoperable across the enterprise.

Key Responsibilities

• Define and own the enterprise AI architecture strategy, covering data (structured/unstructured), model layers, and agentic orchestration
• Architect solutions integrating structured data (relational, data warehouses) and unstructured data (documents, images, video, sensor/IoT streams) into unified AI-ready platforms
• Design agentic AI systems — multi-agent orchestration, tool-use frameworks, propose/review governance models, and human-in-the-loop controls
• Provide architectural guidance on physical AI / embodied AI initiatives (robotics, digital twins, sensor fusion, edge inference) where AI models interact with physical systems
• Establish reference architectures, patterns, and reusable frameworks (RAG pipelines, vector stores, agent skill definitions, model routing/tiering)
• Evaluate and select AI toolchains (LLM providers, orchestration frameworks like LangGraph/AutoGen, MCP-based integrations, vector databases)
• Define governance for AI safety, model access tiers, data security, and compliance (especially around LLM API access, IP, and sandboxing)
• Partner with client stakeholders and delivery teams to translate business requirements into scoped technical solutions and SOWs
• Guide integration of AI into existing enterprise systems (ERP, asset management, monitoring platforms) without disrupting core operations
• Mentor engineering and delivery teams on AI/ML best practices and architectural standards
• Stay current on emerging AI trends (agentic frameworks, small/edge models, robotics AI stacks) and translate them into actionable enterprise roadmaps

Required Skills & Experience

• Strong background in Enterprise Architecture frameworks (TOGAF, Zachman, or equivalent)
• Hands‑on experience with structured data platforms (SQL, data warehousing, ETL/ELT) and unstructured data processing (NLP, computer vision, document AI)
• Practical knowledge of agentic AI architectures — multi‑agent systems, tool/function calling, orchestration frameworks, memory/context management
• Familiarity with physical AI / embodied AI concepts — robotics middleware (ROS), sensor fusion, edge AI inference, digital twins, or industrial IoT integration
• Experience with LLM ecosystems (OpenAI, Anthropic, open‑source models), vector databases (Pinecone, Weaviate, pgvector), and RAG architecture
• Cloud architecture expertise (AWS/Azure/GCP) with AI/ML services (SageMaker, Azure AI, Vertex AI)
• Understanding of AI governance, model risk management, and responsible AI principles
• Experience architecting solutions with security/compliance constraints (data residency, access control, sandboxed execution environments)
• Strong stakeholder management — able to translate architecture into business language for CXO‑level audiences
• Prior experience with pre‑sales/solutioning, scoping, or SOW development is a plus

Preferred / Nice‑to‑Have

• Exposure to industrial/OT environments (manufacturing, energy, utilities) where physical AI is being piloted
• Experience with GitHub Copilot Agent Mode, Copilot CLI, or similar propose‑only agent governance models
• Certifications: TOGAF, AWS/Azure AI certifications, or relevant AI/ML credentials
• Experience in domains like asset management, predictive maintenance, or diagnostics (e.g., DGA/transformer monitoring) is a strong plus

Soft Skills

• Systems thinker with ability to balance innovation against enterprise risk/governance
• Strong communicator across technical and business audiences
• Comfortable operating in ambiguity — many AI use cases (like physical AI) are still maturing

note : Please apply only one month notice period candidates as this position is super urgent