Lead AI Engineer: Autonomous Agents & Platform Architect

Jobtailor; Lahore; On-site; PKR 2,400,000 - 3,600,000; Full time

Job summary

Jobtailor is seeking an experienced engineer to architect autonomous AI agents for multi-step workflows in event-data solutions. You will drive planning, tool orchestration, and production-grade patterns across LangGraph and FastAPI, interfacing with enterprise systems.

You will lead cross-functional teams, mentor engineers, and shape a multi-quarter AI roadmap while ensuring safety, reliability, and scalable integrations with legacy interfaces.

Qualifications

• 6+ years of professional software engineering experience.
• Hands-on production experience building with LangGraph (or comparable agent orchestration frameworks) and FastAPI.
• Experience designing multi-step or multi-agent systems, including task planning, tool orchestration, and error recovery.
• Strong Python skills, with experience designing and shipping LLM-powered applications, including agents, RAG, tool-calling, and structured outputs.
• Experience with MCP (Model Context Protocol) or similar tool/agent-to-application integration patterns.
• Experience with vector databases such as pgvector or Chroma and SQL-based retrieval strategies.
• Solid understanding of LLM application architecture, including prompt design, retrieval strategies, evaluation, and handling model limitations/failure modes in production.
• Judgment about autonomy, including knowing when an agent should act independently versus pause for human confirmation.
• Comfort working across the stack, from API design to deployment, and shipping features end-to-end.
• Strong communication skills and ability to work cross-functionally with product and non-AI engineering teams.
• Nice to have: experience with PHP, Laravel, and Vue.js
• Nice to have: experience with multi-cloud infrastructure
• Nice to have: background in building customer-facing chatbots or support automation
• Nice to have: experience with WebSocket streaming for real-time AI interactions

Responsibilities

• Drive the vision and architecture for fully autonomous AI agents capable of planning, sequencing, and executing multi-step workflows to build complete customer events, including sessions, booths, speakers, and content
• Establish multi-agent architectural standards using LangGraph, defining production-grade patterns for planner/executor models, task decomposition, sub-agent delegation, and graceful failure recovery
• Architect and govern MCP infrastructure, including long-term strategy, security standards, and expansion patterns for agentic systems interfacing with vFairs event data
• Define enterprise-grade safety standards with systemic guardrails, automated validation pipelines, and human-in-the-loop checkpoints
• Establish company-wide evaluation frameworks to benchmark agent accuracy, drift, hallucination rates, cost, and latency
• Lead the full lifecycle of agentic features from R&D prototypes to hardened, production-ready microservices
• Mentor and grow the engineering team through architecture reviews, coding standards for AI services, and increased AI literacy
• Partner with Product and Executive leadership to translate business vision into a multi-quarter AI roadmap
• Lead cross-functional integration with PHP/Laravel/Vue core engineering teams to integrate AI services into legacy architectures and user interfaces

Skills

Python Programming

Multi-Agent Systems Design

LangGraph Experience

FastAPI

LLM Application Architecture

MCP Integration

Vector Databases

SQL

API Design

Team Leadership

Tools

MCP

Pgvector

Chroma

PHP

Laravel

Vue.js

WebSocket Streaming

Job description

Jobtailor is seeking an experienced engineer to architect autonomous AI agents for multi-step workflows in event-data solutions. You will drive planning, tool orchestration, and production-grade patterns across LangGraph and FastAPI, interfacing with enterprise systems.

You will lead cross-functional teams, mentor engineers, and shape a multi-quarter AI roadmap while ensuring safety, reliability, and scalable integrations with legacy interfaces.