Solution Architect

About Us

Automation Anywhere is the leader in Agentic Process Automation (APA), transforming how work gets done with AI-powered automation. Its APA system, built on the industry’s first Process Reasoning Engine (PRE) and specialized AI agents, combines process discovery, RPA, end-to-end orchestration, document processing, and analytics—all delivered with enterprise-grade security and governance. Guided by its vision to fuel the future of work, Automation Anywhere helps organizations worldwide boost productivity, accelerate growth, and unleash human potential.

Qualifications and Skills

• Bachelor’s or Master’s 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:

• Lead the design and architecture of complex RPA solutions with a focus on incorporating Generative AI technologies.
• Development/Coding of Proposed solution. Collaborate with stakeholders to understand business requirements and align automation strategies with organizational goals.
• Develop high-level and detailed solution designs, ensuring they meet scalability, reliability, and security standards.
• Technical ownership of end-to-end engagement as he/she would be completely responsible for the same. Integrate Generative AI capabilities into RPA solutions to enhance customer deliverable, decision-making, learning, and adaptive automation processes.
• Provide technical leadership and guidance to RPA development teams throughout the project lifecycle.
• Conduct feasibility studies and assess the applicability of Generative AI algorithms to optimize automation outcomes.
• Stay abreast of emerging technologies, especially in Generative AI, and evaluate their potential impact on RPA strategies.
• Adaptability and flexibility with respect to technologies, work environment and work timing as we work with global customers.
• Ability to accurately estimate project efforts and timeline during project initiation with appropriate risk, dependency and constraints.
• Open to work from client location and AA office.