AI Lead / Architect – Remote / Flexible

brihatisglobal; United States; Hybrid; USD 140,000 - 190,000; Full time

Job summary

Brihati Global Edge Solutions is seeking an experienced AI Lead / Architect to design and scale end-to-end AI architectures for production-grade applications. The role centers on coordinating AI agents, backend microservices, observability, and security, while aligning technical choices with business strategy.

The ideal candidate will define architectures for conversational AI, guide cross-functional teams, and implement responsible AI governance across enterprise platforms.

Qualifications

• Proven track record in AI/ML and production AI systems.
• Experience with agent orchestration frameworks (LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen).
• Strong knowledge of cloud-native AI platforms (Azure OpenAI/Foundry).
• Ability to design end-to-end AI architectures for enterprise use cases.

Responsibilities

• Design production-ready architecture for conversational AI and agent workflows.
• Guide cross-functional teams across frontend, backend, AI/ML, cloud, and security domains.
• Establish observability, monitoring, and governance for AI pipelines.
• Define integration patterns with enterprise systems and APIs.
• Communicate architectural decisions to technical and executive stakeholders.

Skills

AI/ML expertise

Conversational AI

Agent orchestration

LangGraph / LangChain

Azure AI / OpenAI

Cloud-native architecture

Leadership & guidance

Tools

LangGraph

LangChain

Semantic Kernel

CrewAI

AutoGen

RAG architectures

Vector databases

Job description

Location: Remote / Flexible
Job Category: Information Technology (IT) & Software; Science & Research
Employment Type: Full-Time
Experience Required: Not specified
Company: Brihati Global Edge Solutions

Job Overview

Brihati Global Edge Solutions is seeking an experienced and visionary AI Lead / Architect to design, guide, and scale end-to-end architecture for advanced artificial intelligence capabilities. This role focuses on bringing together AI agents, backend microservices, enterprise integrations, observability, and robust security frameworks to deliver scalable, production-grade applications.

The successful candidate will define architecture for conversational AI experiences and agentic workflows, including interactions between user intents, state management, tool calls, APIs, and backend systems. The role requires expertise in agent orchestration frameworks, cloud-native AI platforms, enterprise integration patterns, and the operational requirements of deploying large language model (LLM)-based applications in production.

In addition to architecture and system design, the AI Lead / Architect will guide cross-functional engineering and product teams, establish observability and responsible AI practices, and align technical decisions with business strategy. This opportunity is suited to a professional who can translate complex AI architecture into practical solutions while supporting collaboration across engineering, product, cloud, security, and operations teams.

Key Responsibilities

Architecture and System Design

• Design high-level, production-ready architecture for conversational AI experiences and agentic workflows.
• Define interactions across user intents, state management, tool calls, APIs, and backend systems.
• Guide the architecture and adoption of LangGraph or equivalent agent orchestration patterns.
• Standardize tool and API integration approaches using Model Context Protocol (MCP) or structured enterprise contracts.
• Design scalable application architectures that integrate AI agents, backend microservices, and enterprise systems.
• Guide technical decisions related to the reliability, maintainability, and scalability of AI-powered applications.

Observability, Security and Responsible AI

• Establish LLM observability requirements to monitor token costs, latency, tool-call execution, failure rates, and quality metrics.
• Define logging, monitoring, and evaluation requirements for production AI systems.
• Enforce data privacy and enterprise security standards across AI pipelines.
• Support auditability and responsible AI governance throughout the AI application lifecycle.
• Guide teams in identifying operational risks and improving the reliability of AI-driven workflows.

Cross-Functional Leadership

• Drive technical decision-making across frontend, backend, AI/ML, cloud, and security teams.
• Collaborate with Product Management, Engineering, Site Reliability Engineering (SRE), Quality Assurance (QA), and Security stakeholders.
• Align system architecture with business strategy, product objectives, and enterprise requirements.
• Communicate architectural decisions, technical trade-offs, and implementation approaches to technical teams and executive stakeholders.
• Provide technical direction to teams building conversational and agentic AI experiences.

Requirements & Qualifications

AI and Agentic Architecture

• Proven track record in AI/ML, conversational AI, or production applications built using large language models.
• Strong expertise or hands‑on experience with agent orchestration frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, or AutoGen.
• Deep understanding of agentic concepts, including intent routing, state persistence, tool calling, fallback strategies, and multi‑agent coordination.
• Ability to design end‑to‑end AI application architectures for production environments.

Cloud and Backend Engineering

• Familiarity with cloud‑native AI platforms, particularly Azure AI services, Azure OpenAI, or Azure AI Foundry.
• Strong foundation in backend API architecture and cloud infrastructure.
• Understanding of enterprise integration patterns and microservices‑based architectures.
• Ability to guide the integration of AI capabilities with existing enterprise systems.

Observability, Evaluation and Governance

• Hands‑on experience or thorough domain understanding of LLM observability, evaluation, logging, and monitoring tools.
• Understanding of production AI quality metrics, operational performance, and failure monitoring.
• Knowledge of enterprise data privacy, security, auditability, and responsible AI governance.

Communication and Leadership

• Exceptional communication skills.
• Ability to explain complex AI architecture to technical teams and executive stakeholders.
• Experience guiding cross‑functional teams through architecture and technical decision‑making.

Preferred Skills

• Experience with Model Context Protocol (MCP) or standardized tool‑based integrations.
• Exposure to vector search, Retrieval‑Augmented Generation (RAG) architectures, and vector databases.
• Experience designing solutions for consumer‑facing platforms, customer relationship management (CRM) systems, contact centers, or enterprise portals.

Salary, Benefits & Career Growth

The employer has not specified the salary range or employee benefits for this position.

This full‑time opportunity offers scope to work on advanced conversational AI, agentic systems, enterprise integrations, and production‑grade AI architecture. Continued professional development in AI orchestration, cloud‑native AI platforms, LLM evaluation, observability, and responsible AI governance may support progression toward principal architect, enterprise AI architect, or AI engineering leadership roles. Specific training programs, certification sponsorship, and employee benefits have not been confirmed.