Senior AI Developer

Position Overview

Auritas is seeking a highly skilled and hands-on Senior AI Engineer Enterprise Transformation to lead the design, development, and operationalization of Applied AI and Generative AI solutions across both consulting delivery and product development organizations. This role is responsible for implementing practical, enterprise-grade AI solutions that improve productivity, quality, scalability, and innovation across SAP environments, document-intensive business processes, workflow automation initiatives, and enterprise software platforms.

This is not a research-focused machine learning role. Instead, it focuses on the implementation of AI and Generative AI technologies within real-world enterprise systems, products, and customer environments. The successful candidate will work closely with product teams, consulting teams, architects, and leadership to embed AI capabilities into how Auritas builds software, delivers services, and develops strategic products.

The employees performance will be evaluated based on solution quality, innovation, project delivery, business impact, customer satisfaction, scalability of AI solutions, technical leadership, and contribution to Auritas AI strategy and product roadmap.

   **Key Responsibilities**       

• 1. 1. Applied AI & Generative AI Solution Design
• Design, develop, and deploy AI- and Generative AI-powered solutions that support enterprise business processes and product innovation.
• Build AI-enabled services for document classification, extraction, validation, summarization, and intelligent workflow automation.
• Implement Large Language Model (LLM)-based solutions utilizing enterprise-grade architectures and secure API integrations.
• Design and develop Retrieval-Augmented Generation (RAG) architectures that improve response quality, contextual accuracy, and business outcomes.
• Develop AI pipelines that integrate intelligent services into SAP processes, enterprise applications, and operational workflows.
• Create and optimize prompts, orchestration frameworks, inference services, and feedback mechanisms.
• Implement human-in-the-loop processes, exception management, and validation controls to ensure solution reliability and trustworthiness.
• Continuously evaluate emerging AI technologies and recommend innovative applications that support Auritas products and services.
  1. 1. Enterprise Integration & Systems Engineering
• Integrate AI services with SAP environments including SAP S/4HANA, SAP ECC, SAP Business Technology Platform (BTP), SAP CPI, APIs, and IDoc-based architectures.
• Connect AI capabilities with enterprise document management, workflow, content management, and business process platforms.
• Design scalable, secure, and compliant enterprise AI architectures.
• Develop and support embeddings, vector databases, semantic search capabilities, and RAG frameworks.
• Ensure AI solutions meet enterprise requirements related to performance, scalability, security, governance, and compliance.
• Collaborate with technical teams to integrate AI services into existing enterprise ecosystems and applications.
  1. 1. Product Development & Delivery Transformation
• Embed AI and Generative AI capabilities into Auritas products, customer-facing solutions, and internal business platforms.
• Identify and prioritize high-value AI use cases within consulting delivery, product development, and operational processes.
• Develop reusable AI components, accelerators, frameworks, and development standards.
• Establish standardized patterns and best practices for AI-enabled service delivery.
• Support AI adoption initiatives and provide technical guidance to development and consulting teams.
• Collaborate with product managers and architects to align AI capabilities with strategic product roadmaps.
  1. 1. AI Governance, Risk & Reliability
• Implement safeguards related to data privacy, responsible AI usage, hallucination mitigation, security, and auditability.
• Design monitoring frameworks that measure AI solution performance, quality, accuracy, and reliability.
• Establish evaluation methodologies for Large Language Models and AI-driven outputs.
• Support enterprise AI governance programs and compliance initiatives.
• Ensure production-grade reliability, scalability, and maintainability of AI-enabled solutions.
• Promote responsible AI practices across product development and consulting organizations.
  1. 1. Technical Leade…