AI Engineer

Experience: 5–8 Years
Employment Type: Full-Time
Role: AI Engineer / Senior AI Engineer
Work Mode: On-site
Department: Engineering / AI & Automation
About the Role
We are looking for an experienced AI Engineer with 5–8 years of software engineering experience and strong hands-on expertise in AI automation, Agentic AI, LLM-based applications, browser automation, MCP servers, and AI developer tooling.
The ideal candidate should be capable of designing and implementing intelligent automation systems using tools such as Claude, OpenAI Codex, n8n, Selenium, Playwright, MCP (Model Context Protocol), Hooks, AI Agents, and Sub-Agents.
This role requires someone who understands not only how to use AI tools, but also how to architect, integrate, optimize, monitor, and productionize AI-powered engineering workflows.
Key Responsibilities
1. Agentic AI & AI Engineering
Design and develop Agentic AI systems capable of autonomous task execution, decision-making, tool usage, and workflow orchestration.
Build AI Agents and Sub-Agent architectures for complex multi-step tasks.
Design agent workflows involving:
Planning
Reasoning
Tool execution
Context management
Task delegation
Validation
Error handling
Human-in-the-loop approval
Develop reusable AI agents for software development, testing, data processing, business automation, and operational workflows.
Evaluate different LLMs and AI models based on quality, latency, reliability, and token/cost consumption.
2. Claude & Codex Engineering Workflows
Hands-on experience with Claude / Claude Code and OpenAI Codex.
Build AI-assisted software development workflows using coding agents.
Understand and optimize:
Context window usage
Input/output token consumption
Cached tokens
Context reuse
Prompt efficiency
Agent execution cost
Model selection
Analyze AI coding-agent usage and identify opportunities to reduce unnecessary token consumption.
Design workflows that intelligently decide when to use:
Main Agent
Sub-Agent
Lightweight model
High-reasoning model
Cached context
External tools
Experience with AI coding-agent workflows involving repository analysis, impact analysis, code generation, testing, debugging, and deployment.
3. MCP Server Development & Integration
Strong understanding of Model Context Protocol (MCP).
Develop, configure, and integrate MCP servers with AI agents and coding assistants.
Build custom MCP tools to expose:
APIs
Databases
Internal systems
File systems
Git repositories
CI/CD systems
Business applications
Design secure tool-access patterns and permission boundaries for AI agents.
Integrate MCP servers with Claude, Codex, and other AI-enabled development environments.
Troubleshoot MCP tool invocation, authentication, context, and integration issues.
4. Hooks & AI Developer Automation
Implement and maintain AI coding-agent Hooks for automated engineering workflows.
Use hooks for activities such as:
Pre-processing
Post-processing
Code validation
Formatting
Security checks
Testing
Git operations
Logging
Context injection
Automated review
Build guardrails around autonomous AI agents to prevent unsafe or unintended changes.
Develop reusable automation patterns for AI-assisted development teams.
5. Browser & Test Automation
Strong hands-on experience with Selenium and Playwright.
Develop robust browser automation frameworks and reusable automation components.
Automate:
Web applications
Functional testing
Regression testing
Data extraction
Form processing
Login/authentication workflows
Business process automation
Prefer Playwright where appropriate for modern web applications.
Implement reliable selectors, waits, retries, parallel execution, screenshots, traces, and failure diagnostics.
Integrate browser automation with AI agents to enable intelligent browser-based task execution.
6. n8n & Workflow Automation
Design and implement business automation workflows using n8n.
Integrate n8n with:
REST APIs
Webhooks
Databases
CRM systems
Messaging platforms
LLMs
MCP servers
Internal applications
Build event-driven workflows and multi-step automation pipelines.
Implement error handling, retries, logging, monitoring, and workflow observability.
Build AI-powered workflows combining n8n + LLM + Agents + APIs + MCP.
7. AI-Powered Software Engineering
Integrate AI into the complete software development lifecycle.
Use AI agents for:
Requirement analysis
Architecture analysis
Code generation
Code review
Test generation
Bug investigation
Documentation
Refactoring
Deployment assistance
Build workflows where AI agents can analyze an existing codebase before making changes.
Implement approval-based workflows for AI-generated code and production changes.
8. Production & Engineering
Design scalable and maintainable AI automation solutions.
Develop APIs and backend services required for AI workflows.
Implement authentication, authorization, secrets management, logging, monitoring, and audit trails.
Work with development teams to integrate AI capabilities into existing enterprise applications.
Ensure AI solutions are reliable, secure, cost-efficient, and production-ready.
Required Technical Skills
AI / LLM
Agentic AI
Generative AI
LLM APIs
AI Agents
Sub-Agents
Prompt Engineering
Tool Calling / Function Calling
Context Management
RAG concepts
AI workflow orchestration
LLM evaluation
Token optimization
AI Developer Tools
Claude / Claude Code
OpenAI Codex
MCP / Model Context Protocol
MCP Server development
MCP Tools
Hooks
AI coding agents
AI-assisted development workflows
Automation
n8n
Playwright
Selenium
REST API automation
Webhooks
Workflow orchestration
Browser automation
Test automation
Software Engineering
Strong programming experience in at least one of:
C#
Python
JavaScript / TypeScript
Java
REST APIs
Git / GitHub / GitLab / Azure DevOps
SQL / relational databases
Docker
CI/CD
Cloud platforms such as Azure / AWS / GCP
Token & AI Cost Optimization
The candidate should have practical experience understanding and optimizing LLM consumption and AI-agent costs.
Expected knowledge includes:
Input vs output tokens
Cached input/context
Context window management
Token estimation
Prompt optimization
Context compression
Agent/sub-agent cost analysis
Avoiding unnecessary tool calls
Selecting appropriate models for different tasks
Managing long-running coding-agent sessions
Monitoring AI usage and identifying expensive workflows
Designing cost-efficient multi-agent architectures
Experience analyzing Claude and Codex usage/consumption will be highly preferred.
MCP & Agent Architecture
The candidate should be comfortable designing architectures such as:
User → AI Agent → Planner → Sub-Agent → MCP Tool → Application/API → Result → Validation → User
and:
n8n → AI Agent → MCP Server → Browser Automation → Playwright → Business Application
The candidate should understand when to use a traditional automation workflow versus an AI agent and when a deterministic workflow is preferable.
Preferred Experience
Experience building production-grade Agentic AI systems.
Experience developing custom MCP servers.
Experience integrating MCP with Claude Code or similar AI coding environments.
Experience building AI-powered developer tools.
Experience with Claude Code Hooks or equivalent agent lifecycle automation.
Experience combining n8n + LLM + MCP + browser automation.
Experience building multi-agent or hierarchical agent architectures.
Experience measuring AI quality, latency, reliability, and cost.
Experience implementing AI guardrails and human approval workflows.
Experience with Azure AI / Azure OpenAI / AWS Bedrock / Vertex AI or similar platforms.
Qualifications
Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related field.
5–8 years of professional software engineering expe

Work arrangement
No