Senior Automation Engineer

Job Description Senior AI Automation Engineer

Position Title

Senior AI Automation Engineer

Department

Technology / Digital Transformation / AI & Innovation

Reporting To

Head of Technology / Chief Technology Officer (CTO) / Director – Digital Transformation

Job Location

As per Company Requirement

Employment Type

Full-Time

Position Overview

We are seeking a highly experienced and innovative Senior AI Automation Engineer to lead the design, development, deployment, and optimization of AI-powered automation solutions across business functions. The ideal candidate will possess strong expertise in Artificial Intelligence, Machine Learning, Generative AI, Large Language Models (LLMs), Robotic Process Automation (RPA), workflow orchestration, APIs, cloud platforms, and enterprise automation tools.

The role requires transforming manual and repetitive business processes into intelligent automated workflows while ensuring scalability, security, efficiency, and measurable business impact.

Key Roles & Responsibilities

1. AI Strategy & Solution Design

• Identify automation opportunities across departments.
• Design end-to-end AI-driven business solutions.
• Recommend suitable AI technologies for different business use cases.
• Develop enterprise AI automation roadmap.
• Collaborate with leadership on digital transformation initiatives.
• Evaluate emerging AI technologies for business adoption.

2. AI Agent Development

Design, build, and deploy intelligent AI agents capable of:

• Customer support automation
• HR automation
• Recruitment automation
• Finance automation
• Sales automation
• CRM automation
• Knowledge management
• Document intelligence
• Internal employee assistants
• Executive AI assistants

Develop autonomous AI systems capable of:

• Decision making
• Task execution
• Multi-agent collaboration
• Tool calling
• Memory management
• Context handling

3. Generative AI Development

Develop AI applications using:

• Large Language Models (LLMs)
• GPT models
• Claude
• Gemini
• Open-source LLMs
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI Chatbots
• AI Copilots
• AI Assistants

Responsibilities include:

• Prompt optimization
• Context engineering
• Response quality improvement
• Hallucination reduction
• Model evaluation
• Fine-tuning strategies
• AI safety implementation

4. Process Automation

Automate business operations including:

• HR workflows
• Employee onboarding
• Leave management
• Payroll approvals
• Invoice processing
• Purchase workflows
• Customer inquiries
• Lead management
• CRM updates
• Email automation
• Reporting automation
• Data synchronization

Replace repetitive manual tasks with intelligent automated workflows.

5. Workflow Automation

Develop automation pipelines using platforms such as:

• n8n
• Make.com
• Zapier
• Microsoft Power Automate
• UiPath
• Automation Anywhere

Responsibilities include:

• Workflow design
• API integrations
• Conditional logic
• Scheduled automation
• Error handling
• Monitoring and optimization

6. API Integration

Develop and maintain integrations with:

• CRM systems
• ERP software
• HRMS
• Accounting platforms
• Email services
• WhatsApp Business API
• Slack
• Microsoft Teams
• Google Workspace
• Microsoft 365
• Third-party SaaS platforms

Ensure secure, scalable, and reliable API communication.

7. AI Model Deployment

• Deploy AI applications to production environments.
• Optimize model performance and latency.
• Implement monitoring and logging.
• Ensure high availability and reliability.
• Manage model lifecycle and version control.

8. Machine Learning & Data Engineering

• Build predictive models.
• Develop recommendation systems.
• Perform data preprocessing and feature engineering.
• Manage datasets for AI applications.
• Evaluate model accuracy and performance.
• Continuously improve model outputs.

9. Enterprise Automation Architecture

Design scalable automation architecture incorporating:

• AI agents
• APIs
• Databases
• Cloud infrastructure
• Authentication
• Security
• Monitoring
• Event-driven workflows
• Microservices
• Queue systems

10. Cloud & Infrastructure

Work with cloud platforms such as:

• AWS
• Microsoft Azure
• Google Cloud Platform (GCP)

Manage:

• AI deployments
• Containerization (Docker)
• Kubernetes
• Serverless functions
• Cloud storage
• GPU infrastructure

11. Security & Compliance

• Implement AI governance.
• Ensure secure handling of confidential data.
• Follow cybersecurity best practices.
• Ensure compliance with applicable data privacy regulations.
• Manage authentication and authorization.
• Conduct security reviews for AI applications.

12. Performance Optimization

• Improve automation speed and reliability.
• Reduce operational costs.
• Optimize AI response accuracy.
• Enhance workflow efficiency.
• Monitor KPIs and system performance.
• Minimize downtime.

13. Cross-functional Collaboration

Work closely with:

• HR
• Sales
• Marketing
• Finance
• Customer Support
• Operations
• Management
• IT Infrastructure Teams

Translate business requirements into scalable AI automation solutions.

14. Leadership Responsibilities

• Mentor junior AI engineers.
• Conduct code reviews.
• Define engineering standards and best practices.
• Drive technical innovation.
• Lead AI automation projects from concept to deployment.
• Coordinate with vendors and external technology partners.

Educational Qualification

• Bachelor's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, or a related field.
• Master's degree is preferred.
• Relevant AI, Cloud, or Automation certifications are an added advantage.

Experience

• 5–10+ years in Software Engineering, AI Development, or Automation Engineering.
• Minimum 3 years of hands-on experience in Generative AI, LLMs, or Enterprise AI Automation.
• Proven experience delivering AI automation projects in production environments.