Anthropic certified AI Large Language Model (LLM) Technology Architect

Overview

In this hands-on role you design and build AI architectures for enterprise-scale systems, spanning classical ML, generative AI, and agentic setups. You translate requirements into robust patterns, components, and ADRs, while collaborating with data and ML engineers to deliver end-to-end AI solutions. You ground AI outputs with enterprise data and governance, balancing innovation with reliability. This role offers impact across implementation, evaluation, and scalable architecture, with a path toward senior leadership.

Responsibilities

• Design and deliver end-to-end software components for AI architectures, from concept to implementation and testing
• Build AI agent architectures (agents, prompts, tools, skills) and multi-agent orchestration with memory systems
• Prototype and validate agent orchestration patterns for task handoffs, state management, and error recovery
• Evaluate design options balancing capability, cost, performance, and reliability
• Develop evaluation strategies for agent/system quality (accuracy, relevance, faithfulness) and translate insights into design improvements
• Architect, implement, and integrate foundation models (fine-tuning, RAG, custom integrations) with cost/performance trade-offs
• Design model adaptation and fine-tuning pipelines using transformer-based architectures
• Develop AI context layer (context graph, ingestion, indexing) and retrieval components grounding outputs in enterprise data
• Implement embedding/vector storage/retrieval in end-to-end RAG pipelines
• Create context assembly and memory components for grounded, accurate outputs
• Identify reusable components and templated patterns to accelerate delivery
• Optimize model usage, inference, caching, and resource utilization for latency and cost targets
• Design and validate systems against non-functional requirements (security, governance, observability)
• Build governance controls (versioning, audit logging, lineage) and model documentation
• Establish observability (logging, tracing, monitoring, alerting, cost tracking) for production health
• Produce architecture artifacts (ADRs, blueprints, diagrams) to guide teams
• Continuously learn and apply new AI design patterns and technologies
• Collaborate with cross-functional teams to translate requirements into architecture decisions

Key requirements

• Active Anthropic certification
• Experience designing and deploying enterprise-grade AI solutions (agentic, generative, classical AI/ML) on at least one cloud vendor
• Hands-on experience with agentic, LLM, and Generative AI
• Proficiency in Python
• Strong foundation in architecting and operationalizing LLM-driven architectures
• Professional experience in engineering, ML, DL, and NLP solutions
• Multiple years as a machine learning architect delivering large-scale data and analytics solutions

• Cross-functional collaboration
• Strong problem-solving and analytical thinking
• Communication and stakeholder management
• Python programming
• Architecting LLM-driven systems
• Agentic and Generative AI

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