AI LLM Technology Principal Architect (All Genders)
Remote • Kronberg, Campus Kronberg 1
Posted 1d ago
Job Location
Kronberg, Campus Kronberg 1
Tech Stack
Remote Work Policy
Fully remote
Categories
LLM Engineer
About the job
As an experienced AI/LLM Architect, you will be at the heart of designing and building advanced AI systems that power the modern enterprise. A critical dimension of this role is owning the design of a comprehensive AI context layer—drawing on enterprise knowledge bases, structured and unstructured data sources, and domain-specific content—to ground AI systems in the realities of each client's business and ensure outputs are accurate, trustworthy, and impactful. As the technical authority on your domains, you will lead architecture decisions and be accountable for ensuring systems meet rigorous non-functional requirements across security, observability, governance, performance, and scalability. Your contributions will be instrumental in shaping how clients adopt and scale AI, pushing the boundaries of what these systems can achieve and delivering measurable, lasting business value.
Responsibilities
- Translate business strategy into a technical vision by defining non-functional requirements for performance, reliability, and cost.
- Lead stakeholder workshops to align on technical feasibility, define project scope, and manage expectations.
- Drive technology selection, evaluating build-vs-buy decisions for AI platforms and foundational models.
- Architect model- and tool-agnostic multi-agent systems governed by an MCP Control Plane.
- Design and implement the Agent Registry as the system of record and the AI Gateway for runtime policy enforcement.
- Design and implement a certification gate for agents entering production, validating identity, policies, and evaluation metrics.
- Design, implement, and abstract core agent services, including a first-class abstracted memory service.
- Architect the end-to-end data pipeline for AI systems, including ingestion, preprocessing, and synchronization for fine-tuning and RAG.
- Design and implement the context layer—spanning knowledge graphs, vector search, and semantic retrieval—for grounded RAG pipelines.
- Architect foundation model adaptation strategies, including dynamic, cost-and-performance-aware model routing and selection.
- Design, implement, and prototype high-throughput, low-latency inferencing solutions using techniques like response caching and request batching.
- Define security, governance, and observability as centrally-enforced, by-design controls for all AI systems.
- Architect a robust, defense-in-depth security framework, including per-agent identity and layered guardrails.
- Design and implement FinOps controls enforced at the AI Gateway, including token budgets and cost-center labeling.
- Establish the framework for comprehensive system evaluation, adopting productized tools and instrumenting observability.
- Define and maintain the enterprise-wide AI reference architecture, reusable design patterns, and a library of approved software components.
- Independently design, implement, build, and deliver proof-of-concept prototypes and foundational software components.
- Produce and own authoritative architecture artifacts, including blueprints, sequence diagrams, design specifications, and ADRs.
- Mentor and guide cross-functional engineering teams on architectural best practices and design patterns.
- Continuously research and integrate emerging AI patterns, frameworks, and technologies.
Requirements
- Significant experience in designing & deploying enterprise-grade advanced AI solutions using agentic, generative, and classical AI/ML with at least one cloud vendor.
- Practical experience in the Agentic, LLM, and Generative AI space.
- Proficient in Python coding.
- Solid foundation in architecting and operationalizing LLM-driven application architecture patterns.
- Professional working experience in coding engineering, machine learning, deep learning, and NLP solutions and applications.
- Several years of hands-on experience as a machine learning architect designing big data, machine learning, and large-scale analytical engineering solutions.
- German language proficiency at B2 level minimum.