AI Architect

About Us

Automation Anywhere is a leader in AI-powered process automation that puts AI to work across organizations. The company’s Automation Success Platform is powered with specialized AI, generative AI and offers process discovery, RPA, end-to-end process orchestration, document processing, and analytics, with a security and governance-first approach. Automation Anywhere empowers organizations worldwide to unleash productivity gains, drive innovation, improve customer service and accelerate business growth. The company is guided by its vision to fuel the future of work by unleashing human potential through AI-powered automation. Learn more atwww.automationanywhere.com

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 & Qualification Criteria

Must Have Skills

• 8-12 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.