DE - AE- AI Architect-SM-GDS04

The opportunity

We are seeking a Senior AI Architect who combines deep Java/J2EE architecture expertise with hands-on AI and Generative AI delivery experience. The role will shape AI-first enterprise solutions, embed intelligent capabilities into existing Java estates, and guide clients from experimentation through secure, production-scale adoption.

The successful candidate will operate at the intersection of enterprise architecture, product engineering, cloud-native modernization and applied AI. They will define target architectures, make technology decisions, lead engineering teams, and advise senior stakeholders on how AI can create measurable business and engineering outcomes.

Key responsibilities

AI and solution architecture

• Define end-to-end architectures for AI-enabled enterprise applications, from data ingestion and retrieval through model orchestration, inference, APIs, user experience and production operations.
• Architect Retrieval-Augmented Generation (RAG), semantic search, knowledge graph, multimodal and agentic AI solutions aligned to business workflows.
• Design multi-agent systems / skills, tool-use patterns and orchestration approaches, including Model Context Protocol (MCP) where relevant.
• Establish architecture blueprints, reusable patterns, non-functional requirements and technology guardrails for scalable, secure and responsible AI adoption.
• Evaluate models, frameworks, data stores and cloud AI services based on quality, latency, security, portability, operability and cost.

Java/J2EE engineering and modernization

• Architect enterprise solutions using Java/J2EE, Spring Boot, Spring MVC, Spring Security and RESTful APIs.
• Integrate LLM-powered services, copilots and AI agents into existing Java applications and business workflows through well-governed APIs and event-driven patterns.
• Decompose monolithic applications into modular services and define modernization pathways that introduce AI without compromising business continuity.
• Design microservices, event-driven and distributed architectures using technologies such as Kafka or RabbitMQ, container platforms and API management.
• Guide engineering teams on code quality, design patterns, performance, resilience, observability, security and maintainability.

Cloud, LLMOps and production engineering

• Design and deploy AI solutions on Azure, AWS or GCP using appropriate managed AI services and cloud-native components.
• Establish CI/CD, DevSecOps, MLOps and LLMOps practices covering prompt, model and configuration versioning; automated testing; deployment; monitoring; evaluation and rollback.
• Define AI quality and evaluation frameworks for relevance, groundedness, safety, latency, reliability and cost.
• Implement observability across application, model and agent layers, and drive continuous improvement using telemetry and user feedback.
• Lead the transition from proof of concept to hardened, scalable production deployment.

Governance, security and responsible AI

• Embed privacy, data governance, access control, content safety, model security, auditability and Responsible AI requirements into solution design.
• Define controls for prompt injection, sensitive-data exposure, unsafe outputs, model misuse and third-party model risk.
• Partner with security, risk, legal and compliance stakeholders to align AI solutions with enterprise policies and applicable regulatory obligations.
• Drive cost transparency and FinOps practices for model usage, infrastructure and multi-cloud AI workloads.

Consulting, leadership and capability building

• Lead discovery workshops, architecture assessments and design sessions with business and technology stakeholders.
• Translate business goals into AI roadmaps, solution options, implementation plans and clear value cases.
• Act as a trusted advisor to client leadership and communicate architecture choices, trade-offs, risks and outcomes in executive-ready language.
• Provide technical leadership for pursuits, proposals, solutioning and estimation, including architecture narratives and delivery models.
• Lead and mentor cross-functional teams of architects, Java engineers, AI engineers, data scientists and platform specialists.
• Create reusable accelerators, reference implementations, knowledge assets and engineering standards for the EAT competency.

Qualifications and experience

• Education: BE/BTech/MCA or equivalent degree in computer science, engineering or a related discipline.
• Overall experience: 15+ years in software architecture, engineering and enterprise solution delivery.
• Java/J2EE depth: 8+ years designing and delivering enterprise-grade Java/J2EE applications, with strong Spring ecosystem experience.
• Architecture depth: 5+ years in microservices, distributed systems, API-led and event-driven architecture.
• AI delivery: 2+ years of hands-on architecture and implementation experience in AI/ML/GenAI initiatives, with evidence of production or enterprise-scale delivery.
• Leadership: Experience leading multidisciplinary engineering teams and owning architecture and delivery outcomes.
• Consulting: Strong client engagement, workshop facilitation, presales, storytelling and senior stakeholder communication skills.

Mandatory technical skills

Capability & Expected proficiency

• Enterprise engineering - Java 8/11/17+, J2EE, Spring Boot, Spring MVC, Spring Security, REST APIs, microservices, design patterns, distributed systems
• Integration and platforms - API management, Kafka/RabbitMQ, event-driven architecture, Docker, Kubernetes, service observability
• GenAI and agentic AI - LLMs, prompt engineering, embeddings, RAG, semantic and hybrid search, AI agents, multi-agent orchestration, context engineering
• AI frameworks - Hands-on exposure to relevant frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI or Haystack
• Data and retrieval - SQL/NoSQL, vector databases such as Azure AI Search, Pinecone, Weaviate, FAISS or equivalent; knowledge graphs such as Neo4j
• Cloud AI - At least one of Azure OpenAI / Azure AI, AWS Bedrock / SageMaker, or Google Vertex AI, with working awareness of the other major clouds
• Operationalization - CI/CD, DevSecOps, MLOps/LLMOps, model and prompt lifecycle, evaluation, monitoring, tracing and production support
• Security and governance - Identity and access, OAuth/OIDC or SAML, data protection, AI safety, Responsible AI, model risk and secure AI service integration

Preferred skills and experience

• Python and AI service development using FastAPI or Flask, alongside the primary Java/J2EE engineering background.
• Fine-tuning concepts, model optimization, quantization, open-source models and multimodal AI.
• GraphRAG, Knowledge-Augmented Generation, enterprise knowledge platforms and advanced retrieval/evaluation patterns.
• Workflow orchestration or BPM platforms such as Camunda, Airflow or Prefect.
• Infrastructure as Code and platform engineering using tools such as Terraform an