AI Workflow Automation Engineer
New
Based in India, Availability to overlap at least six hours per weekday with Eastern Time business hours.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 10+ years of experience in enterprise automation, workflow engineering, integration engineering, or platform architecture.
- Required Skills
- AWSCloud ComputingGCPAzureCI/CDRESTful APIsPrompt EngineeringLLM
Requirements
- Bachelor’s degree in Computer Science Engineering or a related technical field.
- 10+ years of experience in enterprise automation, workflow engineering, integration engineering, or platform architecture.
- Hands-on experience with n8n, Boomi, or similar automation platforms.
- Experience designing API-first and event-driven architectures, including REST APIs, webhooks, OAuth, JWT, and API security.
- Strong knowledge of cloud platforms such as AWS, Azure, or Google Cloud Platform.
- Experience working with AI and machine learning services such as Amazon Bedrock or Azure OpenAI.
- Practical experience with large language models, prompt engineering, and AI agents.
- Strong understanding of governance, monitoring, reliability, and operational excellence practices.
- Excellent communication skills with the ability to lead technical discussions and collaborate with diverse stakeholders.
- Ability to work independently and influence engineering practices across teams.
- Availability to overlap at least six hours per weekday with Eastern Time business hours.
Responsibilities
- Engineer scalable, secure, and resilient automation solutions using n8n and related integration platforms.
- Design hybrid automation architectures combining workflow automation, APIs, event-driven systems, and AI agents.
- Develop AI-enabled workflows that support intelligent decision-making, human-in-the-loop processes, prompt management, and model integrations.
- Establish automation development standards, reusable templates, exception handling strategies, approval workflows, and security practices.
- Implement deployment strategies, CI/CD pipelines, version control, and governance models for automation workflows.
- Build monitoring, logging, alerting, and reliability frameworks to improve workflow performance, uptime, and recovery processes.
- Define responsible AI practices covering model selection, cost management, prompt/version control, data privacy, and auditability.
- Lead technical discussions, architecture reviews, workflow evaluations, and post-incident improvement sessions.
- Mentor engineers and implementation teams on automation engineering, AI agents, prompt engineering, and responsible AI practices.
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