Solution Architect, Professional Services

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.

Key Activities:

• Architect end-to-end AI/ML solutions tailored to business requirements, ensuring scalability and performance.
• Experienced in use case discovery, scoping, and delivering complex solution architecture designs to multiple audiences requiring an ability to context switch in levels of technical depth across AI/ML technologies and platforms.
• Architect production level AI agent/RAG solutions for customers using our unified platform, including end-to-end ML pipelines, inference optimization, integration with cloud-native services and MLOps
• Provide guidance and best practices to development teams on AI agent implementations.
• Serve as the trusted technical advisor for customers developing GenAI solutions, such as RAG architectures on enterprise knowledge repo and ensure high-quality, efficient, and maintainable code for AI agents.
• Oversee the ingestion, transformation, and validation of large datasets for model training and deployment using AA or 3rd party hyper scaler platform.
• Implement and manage CI/CD pipelines for seamless model deployment and monitoring in production.
• Track and improve the performance of AI/ML systems through testing, validation, and iterative improvements.
• Stay updated on advancements in AI/ML and identify opportunities for integrating new techniques into solutions.
• Work closely with product managers, data scientists, and engineering teams to align on technical goals and deliverables.
• Create detailed architectural diagrams, technical documentation, and best practice guidelines.
• Address critical production issues and optimize system reliability and performance.
• Mentor team members and contribute to organizational learning in AI/ML technologies.
  
  Skills & Qualifications Criteria:
  
  Must Have Skills:
• 7-10 yrs of overall IT experience with 5+ years of experience in AI/ML implementations
• Deep knowledge of machine learning, deep learning, and natural language processing algorithms and frameworks (TensorFlow, PyTorch, Hugging Face, etc.).
• Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI
• Hands-on experience with AI/ML services on AWS, GCP, or Microsoft Azure. Have completed certifications from either of these hyper scaler providers.
• Strong programming skills in Python, Java, or other relevant languages;
• Familiarity with RAG (Retrieval-Augmented Generation) methodologies and integrating Generative AI into enterprise applications.
• Hands-on expertise in prompt engineering, RAG, vector DBs and technologies to implement AI agent solutions.
• Expertise in data preprocessing, feature engineering, and working with large-scale data pipelines (Apache Spark, Kafka, etc.).
   

Nice to have skills:

• Strong background on cloud services from various cloud service providers that integrates with AI/ML solutions
• Experience with MLOps tools like MLflow, Kubeflow, or SageMaker.
• Proficiency in deploying, monitoring, and optimizing models in production environments.
• Ability to design scalable, secure, and high-performance AI/ML solutions.
• Strong analytical skills to address complex technical challenges and innovate scalable solutions.
• Ability to articulate technical concepts to diverse audiences, including non-technical stakeholders.
• Experience mentoring engineers and collaborating with cross-functional teams.
• Understanding of data privacy and AI ethics for compliant system designs.