Hello eager tech expert! The OPEX Consultant tears apart, rebuilds and challenges. But between the reengineered process and the deployed solution, there is a critical role: someone who looks at the optimized process and sees the automation architecture who classifies opportunities into RPA, Intelligent Automation, and AI projects, and then builds and deploys those solutions at a speed that traditional development methods cannot match. This is not a traditional solutions architect or business analyst who writes requirements documents and hands them to a development team. This is an AI-native builder – someone who uses vibe coding and AI-assisted prototyping to go from concept to working prototype in days, not months. Someone who uses AI to generate test cases, who leverages LLMs for solution design, who builds bionic agents, and who treats AI not as a project category but as the development methodology itself. The Digital Solutions Consultant is the bridge between process opportunity and production-ready automation. Every solution they deliver must be built faster, smarter, and deployed leaner than anything a traditional development approach could achieve. You’ll break new ground by What You Will Do The Digital Solutions Consultant takes the reengineered, waste-free processes produced by the OPEX Consultant and translates them into deployed automation solutions. The role owns the entire solution lifecycle – from concept mapping through solution design, rapid prototyping, testing, deployment, and stabilization – using AI at every stage to compress timelines by 60–80%. This person is into the right technology bucket (RPA for rule-based repetition, Intelligent Automation for semi-structured decisions, AI/ML for cognitive tasks), design the solution architecture, and drives rapid development using AI-native methods: vibe prototyping, AI-generated test cases, LLM-assisted design, and AI-powered sprint planning. Areas & Tasks 1. Automation Opportunity Classification & Solution Mapping Receive reengineered process specifications from the OPEX Consultant and identify all automation opportunities within the optimized process Classify each opportunity into the right technology category: RPA: rule-based, repetitive, structured data, high-volume tasks suited for both automation Intelligent Automation (IA): semi-structured processes requiring document intelligence, decision logic, or workflow orchestration AI/ML: cognitive tasks requiring natural language processing, prediction, classification, conversational interfaces, or pattern recognition Build a solution concept map for each process: which components are RPA, which are IA, which are AI, and how they integrate into a cohesive automation architecture Prioritize automation opportunities based on complexity, impact, feasibility, and speed-to-deploy 2. AI-Native Solution Design · Design automation solutions using AI-assisted methods – not 40-page requirements documents, but rapid concept maps, architecture sketches, and interactive prototypes · Use LLMs and generative AI to accelerate solution design: prompt-driven architecture exploration, AI-generated integration patterns, and automated design documentation · Create solution designs that are modular, scalable, and integration-ready – designed for the enterprise, not for a demo · Define data requirements, integration touchpoints, exception handling logic, and fallback mechanisms as part of every solution design 3. Vibe Prototyping & Rapid Development · Build working prototypes using vibe coding and AI-assisted development – concept to clickable prototype in days, not weeks · Use AI code generation tools (GitHub Copilot, Cursor, Claude Code, or equivalent) to accelerate development of automation scripts, both workflows, and integration layers · Develop proof-of-concept solutions that stakeholders can interact with early – eliminating the traditional gap between “design approved” and “first working version” · Iterate rapidly based on stakeholder feedback – fail fast, learn fast, ship fast 4. AI-Powered Testing & Quality Assurance Use AI to generate comprehensive test cases from solution designs and process specifications – not manual test script writing Deploy AI-assisted test automation for regression testing, edge case identification, and exception scenario coverage Validate AI/ML model outputs with appropriate accuracy, bias, and drift checks before production deployment Drive UAT with structured test plans that cover happy paths, exception paths, and integration scenarios 5. Hands-On AI & Automation Build · Build and configure RPA bots: attended and unattended automation workflows, orchestration, exception handling, and credential management · Develop AI solutions: LLM-powered agents, intelligent document processing pipelines, NLP-based classification engines, conversational AI interfaces · Design and deploy bionic agents – human-AI collaborative workflows where AI augments human decision-making rather than replacing it entirely · Integrate automation solutions with enterprise platforms (Workday, ServiceNow, People Center, AskHR, and other H2R systems) 6. AI-Powered Sprint Planning & Delivery · Use AI tools for sprint planning: effort estimation, dependency mapping, backlog prioritization, and capacity allocation – replacing manual estimation rituals · Run compressed development sprints (1–2 weeks) with AI-accelerated velocity – what traditional teams deliver in a sprint, this role delivers in half the time · Maintain live solution delivery dashboards with real-time progress, blocker identification, and automated status reporting · Coordinate with the PM on delivery governance, risk escalation, and go-live readiness 7. Solution Deployment & Stabilization · Drive production deployment with structured go-live checklists, rollback plans, and hyper care protocols · Monitor deployed solutions for performance, accuracy, and stability – with automated alerting and drift detection for AI models · Document deployed solutions with architecture diagrams, runbooks, and operational handover materials · Support knowledge transfer to operational support teams for long-term sustainable Key Responsibilities Support the design and maintenance of ontologies (OWL/RDF/SKOS) under guidance of senior engineers. Assist in implementing and updating the Enterprise Knowledge Graph. Help build and maintain data pipelines for integrating enterprise data from systems like SAP and Workday. Collaborate with Data and AI Engineers to optimize semantic context for LLMs and AI-driven workflows. Contribute to the integration of knowledge graphs with AI technologies, including LLMs and vector databases. Learn and apply semantic modeling standards and governance practices. Work as part of cross-functional teams and engage in continuous learning. You’re excited to build on your existing expertise, including : Required Skills & Qualifications 6–12 years in digital solutions, automation engineering, or AI/ML solution delivery with progressive hands-on experience Minimum 3 years of hands-on RPA development or solution architecture on at least one major platform Minimum 2 delivered AI/ML projects: LLM applications, IDP, NLP, conversational AI, predictive analytics, or classification engines Demonstrable experience with AI-assisted development tools (GitHub Copilot, Cursor, Claude Code, Replit Agent, or equivalent) Experience building bionic agents or human-AI collaborative workflows Proficiency in at least one programming language (Python strongly preferred) and familiarity with API integrations Experience in HR technology landscape, H2R processes, or enterprise automation programs Strongly Preferred · Experience with LLM orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent) · Familiarity with enterprise AI platforms (Azure AI, AWS Bedrock, Google Vertex AI) · Experience with intelligent document processing platforms (ABBYY, UiPath Document Understanding, Azure AI Document Intelligence) · Knowledge of responsible AI practices: bias detection, explainability, model governance · Experience in GBS / multi-geography shared services automation programs Create a better #TomorrowWithUs! We value your unique identity and perspective and are fully committed to providing equitable opportunities and building a workplace that reflects the diversity of society. Come bring your authentic self and create a better tomorrow with us. This role is based in Bangalore. But you’ll also get to visit other locations in India and globe, so you’ll need to go where this journey takes you. In return, you’ll get the chance to work with teams impacting entire cities, countries and the shape of things to come. We’re Siemens. A collection of over 379,000 minds building the future, one day at a time in over 200 countries. We're dedicated to equality, and we welcome applications that reflect the diversity of the communities we work in. All employment decisions at Siemens are based on qualifications, merit and business need. Bring your curiosity and creativity and help us craft tomorrow. Find out more about Siemens careers at: www.siemens.com/careers
