AI Engineer – Agentic AI & Automation

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 / Senior AI Engineer with strong software engineering experience and hands-on expertise in Agentic AI, LLM applications, AI automation, MCP, AI coding agents, browser automation, and workflow orchestration.

The ideal candidate should be able to design, build, integrate, optimize, monitor, and productionize AI-powered engineering and business automation solutions using technologies such as Claude/Claude Code, OpenAI Codex, MCP, n8n, Playwright, Selenium, Hooks, AI Agents, and Sub-Agents.

Key Responsibilities

- Design and develop Agentic AI systems, AI Agents, and Sub-Agent architectures for complex multi-step tasks.
- Build agent workflows covering planning, reasoning, tool execution, context management, delegation, validation, error handling, and human approval.
- Develop reusable AI automation for software development, testing, data processing, and business operations.
- Integrate Claude/Claude Code and OpenAI Codex into AI-assisted software engineering workflows.
- Optimize context usage, token consumption, caching, model selection, prompt efficiency, latency, reliability, and AI-agent cost.
- Build and integrate MCP (Model Context Protocol) servers and custom MCP tools for APIs, databases, file systems, Git, CI/CD, and enterprise applications.
- Implement Hooks, guardrails, validation, security checks, testing, logging, context injection, and automated review for AI coding agents.
- Build robust Playwright/Selenium automation for web applications, testing, regression, data extraction, authentication, and business processes.
- Develop n8n workflows integrating APIs, webhooks, databases, CRM, messaging platforms, LLMs, MCP, and internal applications.
- Integrate AI across the software development lifecycle including requirements, architecture, coding, testing, debugging, code review, documentation, refactoring, and deployment.
- Build scalable and secure AI services with authentication, authorization, secrets management, logging, monitoring, audit trails, and error handling.
- Determine when to use traditional automation, deterministic workflows, LLMs, Agents, Sub-Agents, or hybrid solutions.
- Evaluate LLM solutions based on quality, reliability, latency, performance, and cost.

Required Technical SkillsAI / LLM

- Agentic AI, Generative AI,



LLM APIs
- AI Agents & Sub-Agents
- Prompt Engineering
- Tool/Function Calling
- Context Management & RAG
- AI Workflow Orchestration
- LLM Evaluation
- Token & Cost Optimization

AI Developer Tools

- Claude / Claude Code
- OpenAI Codex
- MCP / Model Context Protocol
- MCP Server & Tool Development
- Hooks
- AI Coding Agents
- AI-Assisted Development Workflows

Automation

- n8n
- Playwright / Selenium
- Browser & Test Automation
- REST APIs & Webhooks
- Workflow Orchestration
- Error Handling, Retries & Observability

Software Engineering

- Strong experience with at least one: Python, C#, JavaScript/TypeScript, or Java
- REST APIs
- SQL / Relational Databases
- Git / GitHub / GitLab / Azure DevOps
- Docker
- CI/CD
- Azure / AWS / GCP

Token & AI Cost Optimization

Hands-on understanding of:

- Input/output and cached tokens
- Context window management
- Token estimation and optimization
- Context compression and reuse
- Agent/Sub-Agent cost analysis
- Model selection based on task complexity
- Reducing unnecessary tool calls
- Managing long-running coding-agent sessions
- Monitoring AI usage and expensive workflows
- Cost-efficient multi-agent architectures

Experience analyzing Claude and Codex usage/consumption is highly preferred.

MCP & Agent Architecture

Should be comfortable designing architectures such as:

User → AI Agent → Planner → Sub-Agent → MCP Tool → API/Application → Result → Validation → User

and:

n8n → AI Agent → MCP Server → Browser Automation → Playwright → Business Application

Must understand secure tool access, permissions, authentication, context, integration, and when deterministic automation is preferable to AI agents.

Production & Engineering

- Design reliable, scalable, maintainable, and secure AI automation systems.
- Implement authentication, authorization, secrets management, logging, monitoring, telemetry, and audit trails.
- Build APIs/backend services required for AI workflows.
- Implement AI guardrails and human-in-the-loop approval.
- Troubleshoot and deploy production AI systems.




- Integrate AI capabilities with existing enterprise applications and engineering teams.

Preferred / Nice-to-Have

- Production-grade Agentic AI / Multi-Agent systems
- Custom MCP Server development
- Claude Code / Codex integration
- Claude Code Hooks or equivalent
- n8n + LLM + MCP + Browser Automation
- Azure AI / Azure AI Foundry / Azure OpenAI
- AWS Bedrock / Google Vertex AI
- OpenAI & Anthropic APIs
- Vector Databases & RAG
- LangChain / LangGraph
- Docker / Kubernetes
- Azure Functions / App Services
- GitHub Actions / Azure DevOps
- AI Observability & Telemetry
- Internal AI Developer Platforms

Qualifications

- Bachelor’s/Master’s degree in Computer Science, IT, Engineering, or related field.
- 5–8 years of skilled software engineering experience.
- Strong programming, problem-solving, architecture, and API development skills.
- Proven hands-on experience building AI, automation, and production software solutions.
- Ability to independently design, develop, test, troubleshoot, deploy, and optimize production systems.

Key Success Metrics

- Production-ready AI Agents and automation workflows
- Reduction of manual engineering/business processes
- Reliable MCP integrations and AI tools
- Improved developer productivity using Claude/Codex
- Reduced LLM token consumption and operational cost
- Robust Playwright/Selenium automation
- Maintainable n8n workflows
- Safe, secure, and auditable AI-agent execution
- Successful enterprise AI integrations

ATS Keywords

AI Engineer, Senior AI Engineer, Generative AI, Agentic AI, AI Agents, Sub-Agents, 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, Java, REST API, SQL, Git, Docker, Azure, AWS, GCP, CI/CD, LangChain, LangGraph, Azure OpenAI, AWS Bedrock, Vertex AI.

Pay: ₹45,000.00 - ₹80,000.00 per month

Application Question(s):

- Have you used Claude Skills, If yes what are its skills, artfacts and designs
- Do you know what is Opus?
- Have you used Luna, Tera, Sol, and Astra and Which tool have you worked on?
- What are the token costs used in using Luna, Tera, Astra and Sol?
- What is RAG, in which scenarios we are using RAG's?
- Have you created your website on GIT or GITHub?

Work Location: In person

📌 AI Engineer (Janakpuri)
🏢 OSS Education
📍 Janakpuri