RPA & Intelligent Automation Developer
Job Description
About the Role
We are seeking a skilled RPA & Intelligent Automation Developer to develop production-grade machine learning solutions using UiPath Studio, Power Automate, Automation Anywhere. This is not a research-only role — you'll take models from concept through training, validation, and deployment to production environments serving real users. The ideal candidate brings a strong foundation in statistical learning, experience with UiPath Orchestrator and SAP GUI Automation, and a track record of deploying ML systems that deliver measurable business impact. You'll collaborate with data engineers, product managers, and business stakeholders to identify high-value AI opportunities and ship solutions that scale.
Key Responsibilities
- Own UiPath Studio implementation and optimization — configuration, customization, and ongoing enhancement based on business needs
- Manage Power Automate workflows including setup, user training, and continuous improvement of processes
- Implement and maintain Automation Anywhere ensuring seamless integration with existing systems and workflows
- Design and train machine learning models using RPA & Intelligent Automation best practices and modern architectures
- Build and maintain data pipelines that feed ML models with clean, validated training data
- Deploy models to production with monitoring, alerting, and automated retraining capabilities
- Collaborate with product teams to identify high-impact AI use cases and estimate feasibility
- Conduct model performance analysis — precision, recall, latency, and business impact metrics
- Document model architectures, training procedures, and serving infrastructure for team knowledge sharing
- Stay current with RPA & Intelligent Automation advances and evaluate new techniques for potential adoption
Must-Have Qualifications
- Hands-on experience with UiPath Studio — configuration, customization, and troubleshooting in production environments
- Proficiency with UiPath Orchestrator as part of the RPA & Intelligent Automation development/operations workflow
- 3+ years of RPA & Intelligent Automation experience with models deployed to production environments
- Strong foundation in statistics, linear algebra, and probability theory
- Experience with the full ML lifecycle — data preparation, training, validation, deployment, and monitoring
- Proficiency in Python and common ML frameworks (TensorFlow, PyTorch, or scikit-learn)
- Understanding of MLOps practices — versioning, reproducibility, and automated pipelines
Nice-to-Have Skills
- UiPath Certified RPA Associate certification or equivalent validated credential
- UiPath Certified Advanced RPA Developer certification or equivalent validated credential
- Experience with advanced RPA & Intelligent Automation features: Power Automate, Automation Anywhere, Document Understanding
- Familiarity with the broader RPA & Intelligent Automation ecosystem including SAP GUI Automation and Excel Automation
- Published research, conference presentations, or open-source ML contributions
- Experience with large language models (LLMs), RAG architectures, or generative AI applications
Interview Tips
End-to-End ML Design
Present a business problem and ask the candidate to design a complete RPA & Intelligent Automation solution — data requirements, model approach, evaluation metrics, and deployment plan.
Code Review Exercise
Show them a RPA & Intelligent Automation code snippet with subtle issues (data leakage, incorrect validation split). See if they catch them and explain the implications.
Production Debugging
Describe a scenario where a deployed RPA & Intelligent Automation model's performance has degraded. Ask them to walk through their diagnostic process step by step.
Technical Presentation
Ask them to present a past RPA & Intelligent Automation project — problem, approach, results, and what they'd do differently. Evaluate technical depth and communication clarity.
Typical Team Structure
Team Size
2-4 RPA & Intelligent Automation engineers
Reports To
Head of AI/ML, VP of Engineering, or CTO
Collaborates With
Data Engineering, Product Management, Backend Engineering, Business Intelligence
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