Agentic AI Architect

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Agentic AI Architect-Anthropic based in United Kingdom.

• Architect, develop, deploy, and maintain scalable AI, machine learning, generative AI, and agentic AI solutions for enterprise use cases.
• Define end-to-end architectures covering data ingestion and preparation, model selection, orchestration, APIs, integrations, user experiences, monitoring, security, and governance.
• Partner with business and technical stakeholders to identify high-value AI opportunities, translate requirements into technical designs, and establish delivery roadmaps.
• Lead technical discovery sessions, architecture workshops, design reviews, proof-of-concepts, demonstrations, and solution delivery activities.
• Design and implement LLM-powered applications using Claude, Anthropic APIs, and other appropriate LLM platforms.
• Apply prompt and context engineering techniques, including instruction design, few-shot examples, structured inputs and outputs, response constraints, and long-context management.
• Architect secure RAG solutions using enterprise documents, knowledge bases, databases, and other approved information sources, including ingestion, chunking, embeddings, retrieval, reranking, citations, and response generation.
• Build agentic systems capable of reasoning over enterprise context, using authorized tools, executing multi-step tasks, and coordinating workflows across enterprise applications.
• Define agent roles, task boundaries, permissions, memory and context strategies, approval gates, fallback mechanisms, and escalation paths.
• Integrate AI agents and workflows with ServiceNow, enterprise APIs, cloud services, databases, collaboration platforms, and operational systems.
• Implement human-in-the-loop controls and safeguards for sensitive, high-impact, low-confidence, or exception-based actions.
• Develop machine learning and NLP solutions for predictive analytics, classification, clustering, forecasting, anomaly detection, recommendation, document intelligence, summarization, and intelligent automation.
• Build data-processing, feature-engineering, ETL/ELT, and model pipelines that support reliable training, deployment, monitoring, and data quality.
• Design and implement AI solutions across AWS, Microsoft Azure, and/or Google Cloud, using services such as SageMaker, Lambda, S3, and Vertex AI where appropriate.
• Develop supporting APIs, microservices, automation components, and integrations required to operationalize AI solutions.
• Work with technologies such as Apache Spark, Snowflake, MySQL, PostgreSQL, MongoDB, and comparable data platforms.
• Establish AI evaluation frameworks, test suites, representative datasets, regression testing, observability, monitoring, tracing, alerting, and feedback loops.
• Measure and optimize AI solutions across accuracy, relevance, groundedness, safety, task completion, latency, cost, reliability, and user experience.
• Implement responsible AI and security controls covering data privacy, access permissions, authentication, authorization, encryption, secrets management, auditability, prompt-injection defenses, output validation, and human review.
• Document architecture decisions, system behavior, limitations, risk controls, operating procedures, and support requirements.
• Design AI-enabled experiences and workflow automations for areas such as employee support, customer service, IT operations, knowledge management, chatbots, virtual agents, and workflow automation.
• Create reusable implementation patterns, agent designs, integration components, evaluation assets, and accelerators to support enterprise AI delivery.
• Develop dashboards and executive-ready reporting using tools such as Tableau and Power BI to demonstrate adoption, business value, operational performance, and model quality.
• Travel to clients and conferences as required.

• 10+ years of professional experience applying AI, machine learning, data science, software engineering, or intelligent automation technologies to practical enterprise use cases.
• Strong hands-on programming skills in Python and SQL, with experience using TensorFlow, PyTorch, Scikit-learn, or comparable machine learning 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 principles, 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 (AWS, Azure, GCP).