Agent Architect – Agentic Systems

​

ABOUT XENONSTACK

XenonStack is the fastest-growing

Data and AI Foundry for Agentic Systems

, enabling people and organizations to gain

real-time and intelligent business insights

• We deliver innovation through: -

Agentic Systems for AI Agents

→

Vision AI Platform

→

Inference AI Infrastructure for Agentic Systems

→

Our mission is to accelerate the world’s transition to

AI + Human Intelligence

by designing, deploying, and scaling

enterprise-grade AI agents

that are

trustworthy, explainable, and production-ready

.

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THE OPPORTUNITY

We are seeking an

Agent Architect

to design and lead the

architecture of multi-agent systems

, enabling enterprises to adopt

Agentic AI at scale

.

This role goes beyond traditional software or ML architecture — you will define how

LLMs, tools, memory, and orchestration frameworks

come together to form

reliable, adaptive, and compliant AI agents

.

If you are passionate about

system design, orchestration, and Responsible AI

, this role is your opportunity to shape the foundation of the

AgentOps ecosystem

.

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KEY RESPONSIBILITIES

Architecture Design

• Define

blueprints for agent workflows

, including reasoning loops, tool integration, memory systems, and multi-agent orchestration.

• Architect

scalable, modular, and extensible agentic systems

aligned with enterprise needs.

Context & Memory Orchestration

• Design

context pipelines

using RAG, knowledge graphs, APIs, and multi-tiered memory (short-term, episodic, long-term).

• Ensure efficient

token management and context allocation

for reliability and cost efficiency.

Integration & Ecosystem

• Build frameworks to connect

LLMs, APIs, enterprise data sources, and observability layers

.

• Collaborate with

AgentOps Engineers

to ensure smooth deployment and monitoring.

Reliability & Governance

• Implement guardrails for

safety, compliance, and brand alignment

.

• Ensure architectures meet

Responsible AI and governance standards

.

Innovation & Strategy

• Stay ahead of

emerging frameworks (LangGraph, MCP, AgentBridge, A2A messaging)

and incorporate them into architecture strategy.

• Partner with product managers and executives to

align agent design with enterprise workflows and outcomes

.

Leadership & Mentorship

• Guide

AI engineers, AgentOps specialists, and product teams

in best practices for agent design.

• Mentor teams on

evaluating trade-offs

between accuracy, cost, latency, and compliance.

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SKILLS & QUALIFICATIONS

Must-Have

• 6–10 years in

AI/ML engineering, systems architecture, or enterprise software design

.

• Deep knowledge of

LLM architectures, orchestration frameworks (LangChain, LangGraph, LlamaIndex), and agent design patterns

.

• Strong understanding of

context engineering, RAG pipelines, and vector/knowledge databases

.

• Experience with

multi-agent orchestration frameworks

(MCP, A2A messaging, AgentBridge).

• Proficiency in

Python

and AI development stacks.

• Familiarity with

cloud-native architecture (AWS, GCP, Azure)

and container orchestration (Kubernetes, Docker).

• Solid understanding of

Responsible AI frameworks

and compliance-driven system design.

Good-to-Have

• Background in

Reinforcement Learning (RLHF, RLAIF, reward modeling)

.

• Experience in

enterprise-scale deployments in BFSI, GRC, SOC, or FinOps

.

• Prior experience as a

Solutions Architect, AI Architect, or Technical Lead

.

• Contributions to

open-source frameworks

in multi-agent systems or LLM orchestration.

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WHY SHOULD YOU JOIN US?

Agentic AI Product Company

Design architectures that power

production-grade AI agents

for enterprises worldwide.
2.

A Fast-Growing Category Leader

Be part of of the fastest-growing

AI Foundries

, delivering

Vision AI, AgentOps, and Inference Infrastructure

.
3.

Career Mobility & Growth

Progress into roles like

Principal Architect, Head of AgentOps, or CTO-track leadership

.
4.

Global Exposure

Work on

enterprise-scale agent architectures

with Fortune 500 companies and regulated industries.
5.

Create Real Impact

Architect systems that directly influence

business-critical decision-making and automation

.
6.

Culture of Excellence

Our values —

Agency, Taste, Ownership, Mastery, Impatience, and Customer Obsession

— give you freedom to innovate and accountability to lead.
7.

Responsible AI First

Shape frameworks ensuring agents are not just powerful, but also

trustworthy, safe, and explainable

.

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XENONSTACK CULTURE – JOIN US & MAKE AN IMPACT!

At XenonStack, we believe in

shaping the future of intelligent systems

. We foster a

culture of cultivation

built on bold, human-centric leadership principles, where

deep work, simplicity, and adoption

define everything we do.

Our Cultural Values

Agency

– Be self-directed and proactive.

Taste

– Sweat the details and build with precision.

Ownership

– Take responsibility for outcomes.

Mastery

– Commit to continuous learning and growth.

Impatience

– Move fast and embrace progress.

Customer Obsession

– Always put the customer first.

Our Product Philosophy

Obsessed with Adoption

– Making AI agents enterprise-ready and accessible.

Obsessed with Simplicity

– Turning complex orchestration into seamless, intuitive workflows.

Be a part of our mission to

accelerate the world’s transition to AI + Human Intelligence

— by designing the

architectures that bring enterprise AI agents to life

​

Requirements

• 6–10 years in AI/ML engineering, systems architecture, or enterprise software design.
• Deep knowledge of LLM architectures, orchestration frameworks (LangChain, LangGraph, LlamaIndex), and agent design patterns.
• Strong understanding of context engineering, RAG pipelines, and vector/knowledge databases.
• Experience with multi-agent orchestration frameworks (MCP, A2A messaging, AgentBridge).
• Proficiency in Python and AI development stacks.
• Familiarity with cloud-native architecture (AWS, GCP, Azure) and container orchestration (Kubernetes, Docker).
• Solid understanding of Responsible AI frameworks and compliance-driven system design.
• Background in Reinforcement Learning (RLHF, RLAIF, reward modeling).
• Experience in enterprise-scale deployments in BFSI, GRC, SOC, or FinOps.
• Prior experience as a Solutions Architect, AI Architect, or Technical Lead.
• Contributions to open-source frameworks in multi-agent systems or LLM orchestration.