Agentic AI Architect-Anthropic

About This Opportunity

We are representing a confidential enterprise consulting organization that is building cutting-edge AI capabilities for global clients. Our talent team is seeking an experienced Agentic AI Architect to join their Germany-based practice. This role offers significant technical ownership across the full lifecycle of enterprise AI solutions—from discovery and architecture through deployment, governance, and optimization.

You will design and deliver scalable AI systems that integrate large language models, retrieval-augmented generation, intelligent automation, and agentic workflows into mission-critical enterprise environments. Working alongside client stakeholders, consultants, data engineers, and platform teams, you'll translate complex business requirements into practical, secure, and responsible AI solutions.

Responsibilities

• Architect, develop, deploy, and maintain enterprise-grade AI, machine learning, generative AI, and agentic AI solutions for production use cases
• Define end-to-end system architectures covering data ingestion, model selection, orchestration, APIs, integrations, monitoring, security, and governance
• Partner with business and technical stakeholders to identify high-value AI opportunities and establish delivery roadmaps
• Lead technical discovery sessions, architecture workshops, design reviews, proof-of-concepts, and solution delivery activities
• Design and implement LLM-powered applications using enterprise-grade APIs and platforms, applying advanced prompt engineering, context management, structured outputs, and long-context techniques
• Architect secure RAG solutions using enterprise documents, knowledge bases, and databases—including ingestion, chunking, embeddings, retrieval, reranking, citations, and response generation
• Build agentic systems capable of reasoning, using authorized tools, executing multi-step tasks, and coordinating workflows across enterprise applications
• Define agent roles, task boundaries, permissions, memory strategies, approval gates, fallback mechanisms, and escalation paths
• Integrate AI agents and workflows with enterprise APIs, cloud services, databases, collaboration platforms, and operational systems
• Implement human-in-the-loop controls and safeguards for sensitive, high-impact, or exception-based actions
• Develop machine learning and NLP solutions for predictive analytics, classification, forecasting, anomaly detection, document intelligence, and intelligent automation
• Build data-processing, feature-engineering, and model pipelines that support reliable training, deployment, and monitoring
• Design and implement AI solutions across AWS, Microsoft Azure, and/or Google Cloud using services such as SageMaker, Lambda, S3, and Vertex AI
• Establish AI evaluation frameworks, test suites, observability, monitoring, tracing, alerting, and feedback loops
• Measure and optimize AI solutions across accuracy, relevance, groundedness, safety, task completion, latency, cost, and user experience
• Implement responsible AI and security controls covering data privacy, access permissions, encryption, auditability, prompt-injection defenses, and output validation
• Document architecture decisions, system behavior, limitations, risk controls, and operating procedures
• Create reusable implementation patterns, agent designs, integration components, and accelerators to support enterprise AI delivery
• Develop dashboards and executive reporting using tools such as Tableau and Power BI to demonstrate adoption and business value
• Travel to clients and conferences as required

What We're Looking For

• 10+ years of professional experience applying AI, machine learning, data science, software engineering, or intelligent automation to enterprise use cases
• Strong hands-on programming skills in Python and SQL, with experience using TensorFlow, PyTorch, Scikit-learn, or comparable frameworks
• Proven experience architecting or delivering AI/ML, LLM, RAG, conversational AI, agentic AI, or AI-powered automation solutions
• Strong understanding of software architecture, algorithms, object-oriented programming, functional design, APIs, and integration patterns
• Practical knowledge of LLM application development, including prompt engineering, context management, tokens, embeddings, vector search, RAG, tool use, structured outputs, and model evaluation
• Experience with data-science libraries such as Pandas, NumPy, Matplotlib, and Seaborn
• Hands-on experience with one or more major cloud platforms, particularly AWS, Azure, and/or Google Cloud
• Experience with big-data technologies and cloud data platforms, including Apache Spark and Snowflake
• Strong NLP experience with Hugging Face, transformer models, cloud AI services, or comparable technologies
• Solid understanding of databases, query optimization, data warehousing, ETL/ELT pipelines, and data-quality practices
• Experience with data visualization and reporting tools such as Tableau and Power BI
• Strong knowledge of responsible AI, including privacy, security, human oversight, hallucination mitigation, prompt-injection defenses, and governed deployment
• Excellent communication and stakeholder-management skills across client, consulting, engineering, and AI/ML teams
• Ability to work effectively in a rapidly evolving technology environment
• Experience with leading LLM platforms, APIs, and implementation practices is highly desirable
• Familiarity with Model Context Protocol concepts and secure approaches to connecting AI applications with enterprise tools is a plus
• Experience designing AI agents, multi-agent systems, orchestration workflows, and human-in-the-loop operating models is advantageous
• Familiarity with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or comparable LLM application frameworks is a plus
• Experience with vector databases, semantic retrieval, embeddings, document ingestion, reranking, and RAG evaluation is desirable
• Familiarity with AI observability, tracing, prompt/version management, evaluation frameworks, guardrails, and cost optimization is beneficial
• Experience with MLOps, LLMOps, CI/CD, Docker, Kubernetes, infrastructure as code, and production deployment practices is a plus
• Bachelor's degree in Computer Science, Data Science, Artificial Intelligence/Machine Learning, Engineering, or related technical discipline preferred; equivalent relevant experience also considered
• Advanced degrees and relevant AI, cloud, data, or platform certifications are advantageous

How We Work With You

When you apply through Get A Job.ai, our recruiting team will carefully review your background against the role requirements. If there's a strong match, one of our recruiters will reach out to conduct an initial screening conversation to learn more about your experience, technical background, and career goals.

Once we confirm mutual interest, we will submit your profile to our client for their consideration. They will manage the interview process, technical assessments, and final hiring decisions directly. Please do not contact the client organization independently—all communication and coordination will flow through our team to ensure a smooth and professional experience.

Pay

Compensation details will be discussed during the screening process based on your experience level and the client's budget parameters.

Get A Job.ai is an equal opportunity recruiter. We welcome applications from all qualified candidates regardless of race, color, religion, sex, national origin, age, disability, veteran status, or any other legally protected characteristic. We are committed to creating an inclusive recruitment process where every candidate is treated with respect and professionalism.