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Senior AI Solutions Architect – Agentic AI & Multi-Agent Systems

Webvory

Mohali

5-8 years

Today

$5.8K–8.7K/yr

Full time

Onsite

Skills Required LLM

RAG

Agentic AI

LLM Tool Calling

LangChain

OpenAI APIs

Anthropic APIs

Vector Database

Multi-Agent Systems

AI Solution Architecture

AI Agent Orchestration

API Integration

Business-System Integration

AI Security

AI Evaluation

Description Seeking a Senior AI Solutions Architect to design and build a production-grade AI Manager focused on advanced agentic and multi-agent AI systems.

Role: Senior AI Solutions Architect – Agentic AI & Multi-Agent Systems

Location: Mohali, Punjab | In person

Experience

- Personally designed and built advanced agentic or multi-agent systems in real production environments
- Proven production experience with advanced Agentic AI or Multi-Agent systems
- Experience with AI solution architecture, AI agent orchestration, LLM tool calling, API and business-system integration, AI security, AI evaluation, human-in-the-loop systems, AI observability, memory/state management, production AI deployment, workflow orchestration

Responsibilities

- Design overall architecture for production-grade agentic AI platform
- Determine use of single-agent, multi-agent, deterministic workflow, or hybrid architectures
- Define agent responsibilities, orchestration, communication, state, and execution flows
- Design scalable LLM and tool-calling infrastructure
- Define interaction between business knowledge, memory, live data, business rules, and AI reasoning
- Design complex multi-step agent workflows including planning, delegation, coordination, review, and verification
- Define communication and context sharing among specialized agents
- Design critic/reviewer mechanisms to validate AI-generated outputs
- Handle agent failures, conflicting outputs, uncertainty, and retries
- Design secure tool-calling and API execution architecture




- Integrate AI agents with authorized business systems and live data
- Implement permission-based access and least-privilege controls
- Ensure AI-generated actions are validated before execution
- Design post-execution verification and rollback/failure mechanisms
- Design safeguards against prompt injection and malicious instructions
- Prevent unauthorized AI access to business systems
- Implement role-based permissions and action-level controls
- Design risk-based human approval workflows
- Establish audit logging for decisions, tool calls, approvals, and actions
- Ensure sensitive systems and databases are not directly exposed to LLMs
- Design evaluation frameworks for AI agents and workflows
- Measure task completion, decision quality, reliability, tool usage, and failure rates
- Build automated and human evaluation mechanisms
- Establish monitoring, tracing, logging, and observability
- Design fallback and recovery strategies
- Work closely with existing AI and software engineering team
- Review existing technical architecture and identify gaps
- Create architecture diagrams, technical specifications, and implementation plans
- Guide engineers during implementation
- Conduct architecture and code reviews
- Make key technical decisions around AI infrastructure and orchestration
- Help transition systems from prototype to reliable production deployment

Additional Responsibilities

- Challenge existing assumptions and recommend better architectural approaches
- Explain architectural recommendations and rationale
- Explain when to use agents versus deterministic workflows
- Explain agent communication methods
- Explain management of memory and business knowledge




- Explain secure access to live business data
- Explain control of autonomous actions
- Explain human approval workflows
- Explain AI decision evaluation methods
- Explain prevention of prompt injection and unauthorized actions
- Explain handling of agent failures and conflicting information
- Explain platform scaling strategies as agent and business functions increase
- Provide anonymized evidence of prior AI agent or multi-agent system projects including architecture diagrams, demos, or case studies

Nice To Have

- Experience with LangGraph
- Experience with LangChain
- Experience with MCP
- Experience with OpenAI APIs
- Experience with Anthropic APIs
- Experience with Python
- Experience with FastAPI
- Experience with Vector Databases
- Experience with event-driven architectures
- Experience with Docker and Kubernetes
- Experience with AWS, GCP, or Azure

More Skills Generative AI, Human-in-the-Loop Systems, AI Observability, Memory Management, State Management, Production AI Deployment, Workflow Orchestration, LangGraph, MCP, Python, FastAPI, Event-driven architectures, Docker, Kubernetes, AWS, GCP, Azure

Prepare for this role

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Top 100 Agentic AI Interview Questions

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