Lead AI Engineer

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

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

Requirements

• 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

Core Competencies

Demonstrates expertise in architecting and implementing autonomous AI agents, with a strong focus on multi-agent systems, task planning, and integration with existing infrastructures. Proficient in Python and LangGraph, with a solid understanding of LLM application architecture and evaluation frameworks.

Highest-signal resume keywords

• LangGraph Experience
• Python Programming
• Multi-Agent System Design
• MCP Integration
• AI Feature Lifecycle Management

Hard Skills

• Python
• LangGraph
• FastAPI
• Multi-Agent Systems
• Task Planning
• Error Recovery
• LLM Application Architecture
• SQL
• Vector Databases
• API Design

Soft Skills

• Strong Communication
• Cross-Functional Collaboration
• Mentoring

Industry Keywords

• AI Agents
• Autonomous Systems
• Event Data Integration
• Safety Standards
• Evaluation Frameworks

Tools & Technologies

• MCP
• Pgvector
• Chroma
• PHP
• Laravel
• Vue.js
• WebSocket Streaming