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 (Field 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 Field 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.

Requirements:

What We're Looking For

• 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 analytics experience-comfort working with datasets (CSV, SQL) to prepare training data or evaluate model performance.

• Exposure to cloud services (Azure, AWS, or GCP), especially AI/ML offerings (Cognitive Services, SageMaker, etc.).

• Knowledge of version control systems (Git) and basic CI/CD concepts (e.g., pipelines for deploying Python scripts or RPA packages).