AI Large Language Model (LLM) Technology Architect

Overview

As an hands-on AI Architect, you will design and build enterprise AI systems spanning classical ML, generative AI, and agentic architectures. You’ll translate requirements into concrete design choices, implement reusable components, and own end-to-end delivery in client engagements. You’ll craft and validate architecture artifacts, including ADRs and integration specs, guiding broader engineering teams. Collaborating with data and ML engineers, you’ll develop deep expertise across the AI stack and shape scalable, reliable solutions that ground AI outputs in enterprise data. A strong hook is the opportunity to influence architecture patterns at scale within a leading global professional and科技

Responsibilities

• Independently design, build, and deliver software components end-to-end (architecture to testing)
• Design and implement AI agent architectures including prompts, tools, and memory systems
• Develop multi-agent orchestration patterns with state management and error handling
• Evaluate design options balancing capability, cost, performance, and reliability
• Create evaluation strategies for agent/system quality and translate findings into design improvements
• Architect foundation model integrations (fine-tuning, RAG, custom integration)
• Design model adaptation, fine-tuning pipelines using transformer-based architectures
• Build the AI context layer with context graphs, ingestion pipelines, and retrieval components
• Integrate embedding, vector storage, and retrieval into end-to-end RAG pipelines
• Design context assembly and memory components for grounded outputs
• Identify reusable components and templated patterns to accelerate delivery
• Optimize for cost efficiency and performance (latency, throughput, cost)
• Ensure non-functional requirements: security, guardrails, PII handling, access controls for Responsible AI
• Build governance, versioning, audit logging, and lineage tracking
• Establish observability (logging, tracing, monitoring, alerting, cost tracking) for production systems
• Produce architecture artifacts (ADRs, blueprints, design docs) to guide teams
• Continuously learn and apply new AI patterns and tech while balancing reliability
• Collaborate with cross-functional teams to translate requirements into architecture decisions

Key requirements

• Proven experience designing and deploying enterprise-grade AI solutions using agentic, generative, and classical AI/ML on at least one cloud vendor
• Hands-on experience in agentic, LLM, and Generative AI space
• Proficiency in Python programming
• Strong foundation in architecting and operationalizing LLM-driven application architectures
• Professional experience in engineering, ML, DL, and NLP applications
• Multiple years as a machine learning architect designing large-scale data and analytical engineering solutions

• cross-functional collaboration
• strong problem-solving
• communication of complex designs
• Python
• LLM architectures
• agent-based systems

…