AI Agent Deployment And Workflow Automation Data Engineer

What role will you play in the team

As an AI Agent Deployment and Workflow Automation Data Engineer, you will spearhead the integration of autonomous artificial intelligence agents and complex event-driven automation pipelines into our core enterprise infrastructure. You will design, build, and deploy intelligent agents capable of executing multi-step business workflows, interacting with disparate enterprise APIs, and processing real-time data streams to drive operational efficiency and autonomous decision-making across customer success and financial operations.

What you will do

You will architect scalable orchestration pipelines using tools like n8n, Flowise, and make.com, connecting Large Language Models, vector databases, and enterprise REST APIs into cohesive autonomous agents. You will monitor agent execution logs, handle exception states gracefully, optimize token consumption, and ensure that all automated workflows adhere to strict enterprise security, data privacy, and governance standards.

Key responsibility

• Design, develop, and deploy production-ready AI agents and autonomous workflow systems using n8n, Flowise, and custom Python scripts.
• Integrate diverse enterprise systems, CRM databases, and cloud services using secure RESTful APIs, webhooks, and modern ETL tools.
• Configure and manage database backends including MongoDB, PostgreSQL, and MySQL to store agent state histories and execution logs.
• Implement robust error handling, retry mechanisms, and fallback protocols within automated multi-step Zapier Automation and make.com pipelines.
• Collaborate with machine learning engineers to connect fine-tuned OpenAI API and Claude API endpoints to agentic execution frameworks.
• Perform rigorous security audits and access control configurations for all deployed AI agents handling sensitive customer and financial data.
• Build real-time monitoring dashboards using Google Data Studio and Power BI to track agent success rates, execution latency, and API costs.

Required Qualification and Skills

• Bachelor's degree in Computer Science, Information Technology, Software Engineering, or a related computational field.
• Proven practical experience in building and deploying AI agents, autonomous workflows, and chatbot systems in production environments.
• Expertise in workflow automation platforms such as n8n, Flowise, make.com, and Zapier Automation.
• Proficiency in Python programming, handling JSON payloads, parsing data, and interacting with RESTful APIs and webhooks.
• Strong database management skills encompassing SQL (PostgreSQL, MySQL) and NoSQL (MongoDB) data stores.
• Familiarity with Large Language Models (LLMs), prompt engineering, and integrating OpenAI API and Claude API endpoints.
• Understanding of containerization technologies (Docker) and cloud deployment platforms (AWS, GCP, Azure) for scalable microservices.

Benefits Included

• Highly competitive annual compensation with performance-based bonuses tied to successful automation deployments.
• Comprehensive medical insurance policy including extensive hospitalization coverage and wellness allowances for family members.
• Flexible hybrid work policy allowing a balance between remote productivity and collaborative in-office sessions.
• Dedicated annual education and training budget for attending tech conferences and obtaining cloud and AI certifications.
• Modern, well-equipped office spaces featuring recreational zones, complimentary catering, and cutting-edge hardware setups.
• Supportive, innovation-driven culture encouraging continuous experimentation, rapid prototyping, and internal career mobility.

A Day in the Life

You start your day by analyzing the execution logs of autonomous customer support agents deployed via n8n to identify any API timeout errors or unhandled exceptions. During the mid-morning, you collaborate with the data science team to integrate a newly fine-tuned Claude API endpoint into an automated lead-qualification workflow. After lunch, you write custom Python scripts to handle complex data transformation between MongoDB and an external CRM webhook. You wrap up your day by reviewing performance metrics on a Google Data Studio dashboard and documenting the deployment architecture for newly released AI agents.