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Agentic AI Architect-Anthropic
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Agentic AI Architect-Anthropic
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.
As an Agentic AI Architect, you will shape and deliver enterprise-grade AI solutions that combine generative AI, machine learning, data, cloud technologies, and intelligent automation. You will design scalable architectures that connect Claude and other AI technologies with enterprise knowledge, applications, APIs, workflows, and mission-critical systems. The role offers significant technical ownership, from discovery and architecture through deployment, evaluation, governance, and optimization. You will work closely with client stakeholders, consultants, data engineers, AI/ML specialists, and platform teams to turn complex business needs into practical solutions. A strong focus will be placed on agentic workflows, RAG, tool use, structured outputs, responsible AI, and human oversight. This is an opportunity to work at the forefront of enterprise AI while helping organizations adopt powerful technologies securely and responsibly. Travel to clients and conferences may be required as needed.
Accountabilities
- 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.
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Requirements
- 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, 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, model limitations, and governed deployment.
- Strong architectural thinking, analytical ability, problem-solving skills, and attention to detail.
- Excellent communication and stakeholder-management skills, with the ability to collaborate across client, consulting, engineering, product, data, and AI/ML teams.
- Ability to work effectively in a rapidly evolving technology environment and adapt to changing AI capabilities and enterprise requirements.
- Experience with Claude, the Anthropic API, Anthropic Console, Claude Code, or Anthropic-focused implementation practices is highly desirable.
- Familiarity with Model Context Protocol (MCP) concepts and secure approaches to connecting AI applications with enterprise tools and data 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, model monitoring, and cost optimization is beneficial.
- Experience with MLOps, LLMOps, CI/CD, Docker, Kubernetes, infrastructure as code, and production cloud deployment practices is a plus.
- A Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence/Machine Learning, Engineering, or a related technical discipline is preferred, with equivalent relevant experience also considered.
- Advanced degrees and relevant AI, cloud, data, ServiceNow, or Anthropic certifications are advantageous.
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Benefits
- Opportunity to work on advanced enterprise AI and agentic AI initiatives with global clients.
- Exposure to Claude, Anthropic technologies, generative AI, RAG, intelligent automation, and modern AI engineering practices.
- Access to relevant Anthropic partner enablement, technical training, and product education where available.
- Opportunity to work across AI/ML architecture, cloud engineering, data engineering, enterprise platforms, and workflow automation.
- Collaboration with multidisciplinary and cross-functional teams spanning consulting, engineering, data, AI/ML, and enterprise technology.
- Opportunities to contribute to reusable AI accelerators, architecture patterns, and innovative enterprise solutions.
- Professional growth through exposure to rapidly evolving AI technologies, methodologies, and enterprise use cases.
- Travel opportunities to client engagements and industry conferences as required.
- Inclusive and diverse working environment where different perspectives and contributions are valued.
- Opportunity to work on meaningful projects designed to create measurable business value through responsible AI adoption.
How Jobgether Works
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
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