Solution Architect - RPA Apps

Description :

Qualifications and Skills :

• Bachelors or Masters degree in Computer Science, Engineering, or a related field.
• Proven experience as an RPA Solution Architect, with a strong emphasis on integrating Generative AI into automation solutions.
• Expertise in at least one RPA tools such as Automation Anywhere, UiPath , Blue Prism coupled with proficiency in programming languages (e.g., Python, Java, C#).
• Knowledge and understanding of Generative AI techniques, including deep learning, neural networks, and natural language processing.
• 6 to 10 years of relevant experience.
• Familiarity with process analysis and improvement methodologies. Excellent problem-solving skills and attention to detail.
• Ability to work collaboratively in a team environment and communicate effectively with stakeholders.
• Experience with process documentation and workflow diagramming.
• Experience in designing and implementing scalable, optimized and secure RPA solutions for enterprise-level applications.
• Strong analytical and problem-solving skills, with the ability to translate business requirements into technical solutions.
• Generative Adversarial Networks (GANs) : Proficiency in designing and implementing GANs to generate synthetic data, enhance training datasets, and create realistic simulations for RPA testing.
• Reinforcement Learning : Expertise in applying reinforcement learning techniques to enable RPA systems to learn from experience, make adaptive decisions, and optimize automation processes over time.
• Transfer Learning : Experience in utilizing transfer learning methodologies to leverage pre-trained models and accelerate the training process for Generative AI components within RPA solutions.
• Natural Language Generation (NLG) : Knowledge of NLG algorithms to enable RPA bots to generate human-like and contextually relevant textual content for communication and reporting.
• Explainable AI (XAI) : Understanding of XAI techniques to enhance transparency and interpretability of Generative AI models within RPA systems, ensuring accountability and compliance.
• Model Deployment and Management : Familiarity with deploying Generative AI models in production environments and managing their lifecycle, including monitoring, updates, and version control.
• Data Privacy and Ethics : Knowledge of ethical considerations and data privacy principles related to Generative AI, ensuring responsible and compliant integration within RPA solutions.
• Agentic AI : Knowledge of sentiments, Prompts, and structured input to Agent and proper Structured output from Agent
• Continuous Learning and Research : Commitment to staying updated on the latest advancements in Generative AI through continuous learning, research, and participation in relevant conferences and forums.

Responsibilities :

(ref : hirist.tech)