AI Engineer

About the job

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 experience.
• Strong programming and problem-solving skills.
• Strong understanding of software architecture and API development.
• Demonstrated hands-on experience building automation and AI-based solutions.
• Ability to independently design, implement, test, troubleshoot, and deploy production systems.

What We Are Looking For

We are looking for someone who is not just an AI-tool user, but an engineer who can build systems around AI.

The ideal candidate should be able to:

• Take a business problem and identify opportunities for AI automation.
• Decide whether the problem requires an LLM, Agent, Sub-Agent, n8n workflow, traditional automation, or a combination.
• Build MCP servers and expose tools to AI agents.
• Integrate Claude/Codex into engineering workflows.
• Build browser automation using Playwright/Selenium.
• Create n8n-based automation pipelines.
• Implement Hooks and guardrails around AI coding agents.
• Monitor and optimize token consumption.
• Build reliable, secure, and maintainable production AI systems.

Nice-to-Have

• Experience with Azure AI Foundry.
• Experience with OpenAI APIs.
• Experience with Anthropic APIs.
• Experience with vector databases.
• RAG implementation experience.
• LangChain / LangGraph or similar frameworks.
• Docker/Kubernetes.
• Azure Functions / App Services.
• CI/CD automation.
• Observability and AI telemetry.
• Experience building internal AI developer platforms.
• Experience with GitHub Actions / Azure DevOps pipelines.

Key Success Metrics

Success in this role will be measured by the ability to:

1. Build production-ready AI agents and automation workflows.
2. Reduce manual engineering and business processes through automation.
3. Build reliable MCP integrations and AI tools.
4. Improve developer productivity using Claude/Codex.
5. Optimize LLM token consumption and operational cost.
6. Build robust browser automation using Playwright/Selenium.
7. Deliver maintainable n8n workflows.
8. Implement safe and auditable AI-agent execution.
9. Successfully integrate AI capabilities with existing enterprise systems.

Keywords for Recruitment / ATS

AI Engineer, Senior AI Engineer, Generative AI, Agentic AI, AI Agents, Sub-Agent, LLM, Claude, Claude Code, OpenAI Codex, MCP, Model Context Protocol, MCP Server, MCP Tools, Hooks, AI Automation, n8n, Workflow Automation, Selenium, Playwright, Browser Automation, Prompt Engineering, Tool Calling, Function Calling, RAG, LLM Optimization, Token Optimization, AI Developer Tools, AI Coding Agents, Python, C#, JavaScript, TypeScript, REST API, Git, Docker, Azure, AWS, CI/CD.