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Pearson

Advanced Specialist, Enterprise Architect - (AI Systems & Distributed Engineering)

Pearson

Location
Hybrid (Bangalore, Karnataka)
Employment
Full-time
Level
Senior Level
Posted Today

About the Role

Pearson seeks a hands-on Enterprise Architect to define reference architectures for agentic AI systems and govern enterprise-wide AI workflows. This role bridges strategic governance with technical execution to transform how the enterprise leverages autonomous agents and LLM-driven processes.

Skills

Enterprise Architecture AI Systems Distributed Engineering LLM Application Architecture RAG Pipelines Agent Orchestration Cloud Platforms Kubernetes Infrastructure as Code Microservices API Gateways Data Architecture Data Mesh ArchiMate TOGAF C4 Model

Full job details

Job Title: Advance Specialist ,Enterprise Architect 

About the role:

Seeking a visionary Enterprise Architect to be part of the Enterprise Architecture team. This is not an "advisory" role — we require a practitioner who balances high-level strategic governance with deep-dive technical execution. The ideal candidate will bridge the gap between traditional distributed and enterprise systems and the emerging frontier of Agentic AI, transforming how the enterprise leverages autonomous agents and LLM-driven workflows.

Experience Level: 12-15 Years Location: Bangalore or Chennai, India Role Type: Hands-on Enterprise Architect

Key Responsibilities

  • Architectural Strategy & Blueprint Ownership
    • Own and maintain the enterprise architecture blueprint — current-state and target-state views across application, data, integration, and infrastructure layers.
    • Define and evangelize reference architectures and reusable patterns for agentic AI systems (agent orchestration, tool-calling, memory/state management, guardrails) so delivery teams don't reinvent foundational patterns per project.
  • AI Agent Ecosystems
    • Design and govern the architecture for multi-agent systems, including agent-to-agent communication protocols, orchestration frameworks, and tool/function-calling interfaces.
    • Define standards for grounding, memory, retrieval, and context management across LLM-driven workflows.
    • Establish human-in-the-loop checkpoints and escalation paths for autonomous agents operating on production data or systems.
    • Evaluate and select frameworks/platforms for agent orchestration (e.g., orchestration engines, model routing, guardrail layers), balancing build vs. buy.
  • Modeling & Governance
    • Chair or actively participate in the Architecture Review Chapter; define review cadence, entry/exit criteria, and a clear exception-handling process for teams that need to move fast.
    • Own Architecture Decision Records (ADRs) and maintain a lightweight, non-bureaucratic approval workflow so governance catches drift without blocking delivery.
    • Define non-negotiable guardrails for AI/agentic systems — data access boundaries, PII handling, human-in-the-loop requirements, and audit logging — that any new agent workflow must pass through review.
    • Maintain enterprise modeling artifacts (capability maps, data flow diagrams, integration catalogs) as the single source of truth referenced during reviews.

Required Technical Skills

AI

  • Hands-on experience with LLM application architecture: RAG pipelines, agent frameworks, prompt/context engineering, model evaluation and guardrails.
  • Familiarity with multi-agent orchestration patterns and tool-use/function-calling architectures.

Cloud & Infrastructure

  • Deep experience with at least one major cloud platform (AWS/Azure/GCP), containerization (Kubernetes), and infra-as-code.
  • Experience designing for scalability, resilience, and cost efficiency at enterprise scale.

Distributed & Enterprise Systems Integration

  • Strong background in distributed systems design (event-driven architecture, microservices, API gateways, messaging systems).
  • Experience integrating legacy enterprise systems (ERP, CRM, core platforms) with modern cloud-native and AI-driven components.

Data Architecture

  • Experience designing enterprise data architectures — data pipelines, data mesh/data fabric concepts, governance, and lineage.
  • Ability to model and maintain the data-flow view that underpins the enterprise digital twin.

Modeling & Governance Tooling

  • Practical experience with architecture modeling notations/tools (e.g., ArchiMate, C4 model, TOGAF-aligned frameworks) and running formal EA review processes.

 

Qualifications

  • 12 - 15 years of progressive experience in software engineering and solution architecture.
  • Demonstrated ability to strategise and develop PoC and add create practices around it.
  • A portfolio of successful large-scale modernization or AI-transformation projects, ideally including experience participating in an EA governance process (ARB, ADRs) from scratch or maturing an existing one.
  • Prior experience building or working with an enterprise digital twin or equivalent simulation/dependency-modeling capability is a strong plus.