As an Entry-Level RPA & AI Engineer focused on practical automation, you will translate real business challenges into scalable, hybrid AI-RPA solutions. Working alongside experienced AI practitioners, RPA architects, and cross-functional stakeholders, you will:
1. Understand Business Needs
o Partner with stakeholders (Production Service, Sales, Operations) to gather requirements, define goals, and map out where AI-powered decisioning and RPA bots can create the greatest impact—such as automating service ticket routing, parts ordering, or routine report generation.
2. Select & Configure Models and Bots
o Evaluate pre-built AI/ML offerings (NLP, computer vision, predictive analytics) and RPA components.
o Customize settings, prompts, or pipelines to fit specific use cases—e.g., configuring an AI sentiment model to classify customer emails and designing a corresponding RPA workflow to update tickets in the CRM.
3. Prototype & Test Integrated Solutions
o Quickly assemble proof-of-concepts using low-code platforms (e.g., Power Automate AI Builder, UiPath StudioX) or lightweight Python scripts.
o Demonstrate end-to-end automation—such as using an AI model to extract key fields from incoming documents and then triggering an RPA bot to populate those fields into internal systems.
4. Collaborate on Implementation
o Work with software developers and RPA teams to embed AI APIs into automation scripts, ensuring seamless data flows and robust error handling.
o Participate in code reviews and design sessions to align on best practices for modular, maintainable workflows.
5. Validate, Iterate & Improve
o Measure solution impact against KPIs—accuracy of AI classification, bot success rate, time saved, user satisfaction.
o Solicit feedback, refine AI prompts, adjust thresholds, enhance bot logic, and retrain models as needed to drive continuous improvement.
6. Document & Train End Users
o Create step-by-step guides and video demonstrations for internal users to understand both the AI outputs and RPA processes.
o Conduct training sessions and “lunch-and-learn” workshops to foster broader adoption of automation tools.
7. Monitor Production Automations
o Track automated solution performance—bot run logs, AI model drift, exception rates—and collaborate with support teams to remediate issues promptly.
o Generate periodic reports summarizing usage metrics, ROI, and suggested next phases for scaling.
8. Stay Informed & Advise on Strategy
o Keep up with emerging AI frameworks, RPA trends, and best practices.
o Recommend new features or services (e.g., next-gen conversational AI, cognitive document processing, intelligent process mining) that could further enhance Production Service operations.
This role is perfect for someone who’s eager to make an immediate business impact by applying AI and RPA—no deep algorithm-building required—while gaining hands-on experience with real-world deployments. You will develop a broad skill set spanning low-code automation, cloud AI services, and process optimization methodologies.
We’re looking for a curious, self-motivated RPA & AI Engineer to join our Production Service Innovation team and help build next-generation intelligent solutions. In this hands-on role, you’ll partner with senior engineers and RPA specialists to design, develop, and deploy AI models and automation workflows that streamline and optimize core business processes.
Key Responsibilities
1. Identify Automation Use Cases
o Collaborate with Production Service, Sales, and Operations teams to pinpoint opportunities where AI and RPA can streamline workflows, reduce manual effort, and add measurable value.
o Map existing manual or semi-automated processes—such as ticket triage, document handling, parts ordering—and propose end-to-end automation solutions combining AI insights with RPA bots.
2. Evaluate Tools & Platforms
o Research, compare, and recommend AI services (e.g., Azure Cognitive Services, Power Automate AI Builder) and RPA platforms (e.g., Microsoft Power Automate, UiPath, Automation Anywhere).
o Assess pre-built models, low-code/no-code RPA components, and cloud-based AI APIs to determine the best fit for each use case.
3. Prototype Integrated Solutions
o Rapidly assemble proof-of-concepts, using low-code platforms or scripts, that demonstrate AI-driven decisions feeding into RPA workflows (e.g., an AI model classifying incoming service requests, then triggering a bot to route or escalate tickets).
o Leverage Python scripts, Azure Logic Apps, or Power Automate flows to simulate end-to-end automation before full deployment.
4. Design & Implement RPA Workflows
o Develop robust RPA bots to handle repetitive tasks—such as data extraction, form filling, report generation, and system‐to‐system transfers—ensuring they integrate seamlessly with AI modules.
o Collaborate with RPA architects to build, test, and deploy automation pipelines that interact with SAP, Dynamics 365, or other ERP/CRM systems used by Production Service.
5. Integrate AI & RPA into Existing Systems
o Work closely with software and RPA teams to embed AI inference APIs into automation workflows and dashboards, enabling a smooth, end-user experience (e.g., generating automated summaries of service logs, auto-assigning field tickets, or flagging anomalies).
o Ensure reliable data handoffs between AI components and RPA bots—managing triggers, handling exceptions, and logging outcomes.
6. Measure Success & Optimize
o Define KPIs that encompass both AI performance (accuracy, recall, precision) and RPA metrics (bot run times, error rates, process throughput).
o Continuously monitor performance, gather feedback, refine AI prompts or model parameters, and optimize RPA workflows for resilience and maintainability.
7. Train, Enable & Support Users
o Create concise how-to guides, standard operating procedures (SOPs), and live demos to help internal teams adopt AI-RPA solutions confidently.
o Provide hands-on support to troubleshoot common issues—such as failed bot runs or unexpected AI predictions—and update documentation as processes evolve.
8. Monitor & Report
o Track solution usage metrics, uptime, and ROI; prepare clear, concise reports and presentations that highlight wins (time saved, error reduction), lessons learned, and next steps.
o Present progress updates to stakeholders, recommend scaling successful automations, and identify areas for further innovation.
9. Stay Current & Drive Innovation
o Continuously survey emerging AI frameworks, RPA best practices, and industry trends; recommend enhancements or new services (e.g., integrating a new version of an LLM or leveraging an RPA cloud orchestrator).
o Participate in internal innovation forums, hackathons, and external conferences to bring fresh ideas back to the team.
Preferred Skills:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.
- Strong programming skills in Python (experience with libraries such as TensorFlow, PyTorch, or scikit-learn is a plus).
- Solid grasp of core AI/ML concepts (algorithms, evaluation metrics, overfitting/underfitting).
- Familiarity with RPA fundamentals—basic understanding of how to design, test, and deploy bots using platforms like Power Automate or UiPath.
- Eagerness to learn new tools and frameworks; prior professional experience is helpful but not strictly required.
- Excellent problem-solving mindset, logical thinking, and a collaborative spirit.
Strongly Desired:
· Hands-on experience(academic or personal projects) with low-code/no-code RPA platforms (e.g.,building simple bots to automate desktop tasks or data extraction).
· Data analyticsexperience—comfort working with datasets (CSV, SQL) to prepare training data orevaluate model performance.
· Exposure to cloud services(Azure, AWS, or GCP), especially AI/ML offerings (Cognitive Services,SageMaker, etc.).
· Knowledge of version controlsystems (Git) and basic CI/CD concepts (e.g., pipelines for deploying Pythonscripts or RPA packages).
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