Principal Enterprise Architect (AI & Agentic Systems)

DigitalXNode; Bengaluru; Hybrid; INR 4,500,000 - 7,000,000; Full time

Job summary

DigitalXNode is recruiting a Principal Enterprise Architect to lead the design and delivery of enterprise AI platforms in a fully hybrid setup across India. The role commands deep expertise in multi-agent architectures, LLMs, and memory frameworks to enable secure, scalable, and compliant AI workflows.

You will partner with engineering, product, legal, and risk teams to drive architectural vision, set standards, and guide cross-functional teams toward impactful AI implementations.

Qualifications

• Experience designing AI agents and autonomous workflows.
• Hands-on in LLMs, prompt engineering, tool integration, and AI orchestration.
• Proven governance, security, and compliance for AI systems.

Responsibilities

• Define enterprise-wide intelligent agent strategy and architecture.
• Design scalable multi-agent AI systems with LLMs, APIs, tools, and memory layers.
• Enforce architectural standards, safety, and governance for AI platforms.
• Build AI platforms using RAG and memory architectures integrated with enterprise apps.
• Collaborate with engineering, product, legal, risk to deliver scalable AI solutions.
• Align technical priorities with business objectives and innovation goals.

Skills

LLM knowledge and AI orchestration

Kubernetes

APIs & integrations

Cloud platforms

Event-driven architectures

Cross-functional collaboration

Memory frameworks

RAG architectures

Education

Bachelor's degree in CS/Engineering

Master's degree (plus)

Tools

LangGraph

AutoGen

CrewAI

Semantic Kernel

Temporal

Apache Airflow

AWS Step Functions

Job description

Company Overview

We are a talent acquisition and staff augmentation firm, and we are currently hiring on behalf of one of our clients, an organization in the healthcare and life sciences technology space. Our client is looking to onboard a Principal Enterprise Architect across multiple locations in India for a fully Hybrid role that can be offered on a full-time or contractual basis, depending on the candidate's preference and eligibility.

Job Summary

As a technical leader, you will lead the design and implementation of multi-agent architectures by combining LLMs, APIs, tool integrations, and memory frameworks to deliver advanced AI capabilities.

Key Responsibilities

• Drive the technical strategy and architectural direction for enterprise-wide intelligent agent-based solutions, ensuring alignment with business objectives and innovation goals.
• Design and implement scalable multi-agent AI architectures by integrating large language models (LLMs), APIs, external tools, and memory frameworks to enable intelligent, autonomous, and human-assisted workflows.
• Develop and enforce robust architectural standards, operational controls, and safety frameworks to ensure AI systems remain secure, reliable, compliant, and aligned with governance and ethical principles.
• Build and optimize scalable AI platforms leveraging Retrieval-Augmented Generation (RAG), structured memory architectures, and seamless integration with enterprise applications, data sources, and business tools.
• Partner with engineering, product, and cross‑functional teams to provide technical leadership, establish architectural best practices, and drive the successful delivery of enterprise AI solutions.
• Work with cross‑functional teams, including legal and risk stakeholders, to ensure alignment across technical and business priorities.

Required Skills

• Proven expertise in designing and deploying AI agents and autonomous workflows, with strong knowledge of Large Language Models (LLMs), prompt engineering, function calling, tool integration, and AI orchestration techniques.
• Extensive experience working with vector databases, embedding models, and Retrieval-Augmented Generation (RAG) architectures, complemented by hands‑on expertise in distributed systems, APIs, and enterprise integrations.
• Practical experience leveraging cloud platforms and Kubernetes to build and deploy scalable AI applications, with familiarity in event‑driven architectures and workflow orchestration tools such as Temporal, Apache Airflow, or AWS Step Functions.
• Strong understanding of modern multi‑agent orchestration frameworks, including LangGraph, AutoGen, CrewAI, and Semantic Kernel, with experience building collaborative and scalable AI agent ecosystems preferred.
• Hands‑on experience implementing AI observability solutions to monitor prompt execution, optimize operational costs, analyze failures, and improve system reliability.
• Demonstrated expertise in designing scalable system architectures, orchestrating complex workflows, and implementing automation solutions that deliver secure, reliable, and high‑performing enterprise applications.
• Exceptional communication and cross‑functional collaboration skills, with proven ability to partner effectively with engineering, product, legal, compliance, and risk teams to deliver strategic technology initiatives.

AI Experience

• 10+ years of experience in software engineering or platform architecture, including at least one year of hands‑on experience designing and implementing solutions powered by Large Language Models (LLMs), AI platforms, or machine learning technologies.
• Demonstrated expertise in establishing AI governance, security, and compliance frameworks, with proven experience implementing guardrails that promote safety, reliability, regulatory compliance, and responsible AI practices.
• Proven track record of architecting and deploying event‑driven and distributed systems, with hands‑on experience in message queuing, asynchronous communication, and real‑time data processing for scalable enterprise applications.

Managerial Experience

• Strong expertise in leading engineering and product teams, shaping architectural vision, and promoting best practices to deliver scalable, high‑quality solutions.
• Ability to guide and influence cross‑functional teams without direct reporting authority.

Operational Experience

• Track record of building and scaling platforms that support business‑critical, production‑grade AI workflows.
• Experience establishing observability, monitoring, and failure‑analysis practices for AI systems in live environments.
• Familiarity with maintaining compliance and governance standards across evolving AI regulations.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field; a Master's degree is a plus.
• 10+ years of experience in software or platform architecture.

Certifications

• Relevant cloud, AI/ML, or enterprise architecture certifications (e.g., TOGAF, AWS/Azure/GCP architecture certifications) are a plus, though not mandatory.