AI Solutions Architect

About the Job
As a specialist in electronics and software for the past 20 years, in-tech is a dynamic, fast-growing engineering company headquartered in Munich, Germany, employing around 2,350 employees globally across 20 project locations in 8 countries. in-tech develops innovative engineering solutions for the automotive, rail, aerospace, defence, and industrial sectors.

We are committed to a flexible, modern work culture and work-life balance. Our colour orange stands for liveliness, warmth, and dynamism. We value a strong team spirit, fresh ideas, and a positive work culture. We call it the “Orange Spirit”!

Since 2024, in-tech has been a subsidiary of Infosys Ltd. This strategic partnership enables us to offer our customers even more comprehensive development and digitalisation services, along with greater offshore capability.

We are looking for an experienced AI-Enabled Solutions Architect / AI Architect to join our team. In this role, you will lead the architecture, design, and delivery of scalable software products, cloud platforms, enterprise AI systems, and AI-enabled modernization programs. You will work with enterprise customers and internal stakeholders to identify business transformation opportunities and translate them into secure, scalable, and value-driven solutions using Generative AI, Agentic AI, Predictive AI, and intelligent automation. If you are passionate about enterprise architecture, emerging AI technologies, cloud-native engineering, and creating measurable business impact in a growing organization with a positive culture and strong team spirit, we would love to connect with you!

Responsibilities

• Architect and lead the design of software products, SaaS platforms, cloud-native solutions, distributed systems, and enterprise AI ecosystems.
• Identify business transformation opportunities using Generative AI, Agentic AI, Predictive AI, and intelligent automation, and translate them into scalable solution architectures.
• Design and develop end-to-end AI and GenAI solutions using LLMs, RAG, Agentic RAG, embeddings, vector databases, prompt engineering, and enterprise knowledge management platforms.
• Architect AI agents, multi-agent systems, agent workflows, and orchestration solutions using frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, or similar technologies.
• Define and implement enterprise AI orchestration patterns, including MCP, A2A communication, agent supervision, human-in-the-loop controls, and governance mechanisms.
• Design modern application architectures using Java, Python, APIs, microservices, event-driven architectures, and enterprise integration patterns.
• Lead the architecture and technical execution of AI-enabled modernization programs, including legacy application assessment, code transformation, refactoring, migration, and platform modernization.
• Apply AI across the software development lifecycle, including requirements analysis, architecture, development, testing, DevSecOps, operations, monitoring, and support.
• Architect and implement cloud-based AI and software platforms on AWS, Microsoft Azure, Google Cloud Platform, Databricks, and Kubernetes-based environments.
• Define platform engineering, DevOps, MLOps, LLMOps, CI/CD, Infrastructure-as-Code, containerization, observability, and operational readiness practices.
• Integrate AI solutions with enterprise platforms such as SAP, Oracle, Salesforce, ServiceNow, and custom business applications.
• Establish responsible and secure AI architectures covering privacy, security, compliance, risk management, model governance, data protection, and enterprise AI guardrails.
• Define AI business cases, value realization frameworks, KPIs, adoption strategies, and ROI models for enterprise AI initiatives.
• Lead AI initiatives from opportunity assessment and proof of concept through production deployment, scaling, adoption, and business value realization.
• Develop reusable AI accelerators, reference architectures, frameworks, engineering standards, and best practices.
• Collaborate with enterprise architects, product managers, engineering teams, security teams, data specialists, and C-level stakeholders to define AI roadmaps and solution strategies.
• Facilitate architecture workshops, design-thinking sessions, technical assessments, executive discussions, and customer presentations.
• Evaluate emerging AI technologies, platforms, frameworks, and agentic tools, and assess their suitability for enterprise adoption.
• Provide technical leadership and mentorship to architects, developers, data scientists, and engineering teams.
• Drive measurable improvements in productivity, software quality, customer experience, operational efficiency, and business performance through AI-enabled solutions.

Requirements

• Bachelor’s or master’s degree in Computer Science, Engineering, Information Technology, Business Informatics, or a comparable technical field.
• 10+ years of experience in software architecture, product architecture, enterprise architecture, cloud architecture, or technology consulting.
• Proven experience architecting and delivering software products, SaaS platforms, enterprise applications, distributed systems, and cloud-native solutions.
• Strong hands-on expertise in Java, Python, modern full-stack technologies, APIs, microservices, event-driven architectures, and enterprise integration patterns.
• Proven experience designing and implementing Generative AI, LLM, RAG, Agentic RAG, or intelligent automation solutions for enterprise environments.
• Strong knowledge of LLM concepts, prompt engineering, embeddings, vector databases, model selection, fine-tuning approaches, evaluation methods, and enterprise knowledge management.
• Hands-on experience designing AI agents, multi-agent systems, and orchestration workflows using LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, or similar frameworks.
• Strong understanding of MCP, A2A, agent governance, human-in-the-loop workflows, agent supervision, AI orchestration, and enterprise AI operating models.
• Experience applying AI across the SDLC, including requirements engineering, code generation, architecture analysis, testing, DevSecOps, operations, and application support.
• Proven experience leading legacy modernization initiatives, including application assessment, code transformation, refactoring, re-platforming, migration, and modernization strategy.
• Strong expertise in one or more major cloud platforms, including AWS, Microsoft Azure, or Google Cloud Platform.
• Experience with Databricks, Kubernetes, Docker, CI/CD, DevSecOps, Infrastructure-as-Code, platform engineering, observability, and cloud security.
• Experience with MLOps, LLMOps, model deployment, model monitoring, AI evaluation, and production operationalization of AI systems.
• Practical experience integrating AI and software solutions with enterprise platforms such as SAP, Oracle, Salesforce, ServiceNow, or custom business applications.
• Strong understanding of Responsible AI, AI security, data privacy, compliance, risk management, model governance, and enterprise AI guardrails.
• Experience defining AI business cases, KPIs, ROI models, value realization frameworks, and enterprise adoption strategies.
• Proven ability to take AI initiatives from opportunity assessment and PoC through production deployment, scaling, and measurable business outcomes.
• Experience creating reusable AI accelerators, reference architectures, frameworks, technical standards, and engineering best practices.
• Strong consulting, stakeholder management, workshop facilitation, executive communication, and cross-functional leadership skills.
• Familiarity with architecture frameworks and agile delivery methods such as Scrum, Kanban, SAFe, or similar approaches.
• Strong analytical, problem-solving, decision-making, and presentation skills.

Apply with us

If you have experience and team spirit and are looking for a great place to work, then start your job with us.

As part of our dedication to the diversity of our workforce, in-tech is committed to equal employment opportunity without regard for age, race, colour, national origin, ethnicity, gender, protected veteran status, disability, sexual orientation, gender identity, or religion