AI Application Architect
Role Summary
Serve as the architectural leader defining the reference architecture, guardrails, and delivery patterns for enterprise-grade AI applications. Own the end-to-end architecture across application layers, data, model lifecycle, integration, security, and operations—balancing performance, cost, compliance, and developer productivity. Combine deep application architecture expertise with hands-on Large Language Model (LLM) solution design, retrieval-augmented generation (RAG), and platform-scale governance.
What You’ll Do
Application Architecture
Define, document, and evolve the target-state architecture for AI-enabled applications and services.
Design modular, domain-aligned services (microservices, event-driven, API-first), with clear SLAs/SLOs and versioning strategies.
Establish canonical integration patterns for LLM invocation (synchronous, streaming, async, batch) and system-of-record interactions.
Conduct architectural runway planning, ADRs (Architecture Decision Records), and blueprint creation for product teams.
Collaborate with Solution and Enterprise architects. Present architecture designs, trade‑offs, and risk assessments to the Architecture Review Board and other governance forums for approval and alignment.
AI & LLM Solution Design
Architect production LLM solutions using RAG, tools/agents, embeddings, and vector search; select fit-for-purpose models (hosted, open-source, proprietary).
Define prompt orchestration and safety layers (prompt templates, guardrails, red-teaming, policy enforcement).
Partner with Data Science/ML Engineering on model selection, fine-tuning vs. prompt-tuning, distillation, and deployment strategies.
Design evaluation and feedback loops and telemetry for continuous improvement.
Platform & LLMOps Enablement
Define platform capabilities for prompt/version management, evaluation harnesses, model registries, and feature/embedding stores.
Standardize CI/CD for AI.
Partner with Platform/DevOps to design observability across application + model layers (traces, metrics, logs, eval KPIs).
Technical Leadership & Mentorship
Collaborate with Product, Data, Security, Compliance, and SRE to align architecture with business outcomes.
Coach teams on AI application patterns, anti-patterns, and platform reuse to accelerate delivery.
Innovation & Research
Evaluate emerging models, frameworks, and vector/agent tooling; run POCs that de-risk delivery and quantify value.
Curate a living reference architecture and pattern library; drive continuous modernization of the AI stack.
Quality & Delivery Excellence
Enforce architecture fitness functions and automated checks in CI/CD.
Champion infrastructure-as-code, policy-as-code, and automated governance.
Ensure high-quality documentation of interfaces, contracts, runbooks, and operational playbooks.
Qualifications & Experience
10+ years in software/application architecture or senior engineering roles delivering complex, distributed systems.
2–4+ years hands-on with AI/LLM solution architecture or production ML systems.
Proven track record designing secure, scalable, compliant enterprise applications.
Experience guiding multi-team programs and influencing senior stakeholders.
Technical Skills
Architecture: Distributed systems, microservices, APIs, event-driven patterns, streaming, resiliency, and caching.
AI/LLM: prompt engineering/orchestration, agentic patterns, evaluation methodologies, model deployment, RAG, embeddings, vector databases.
LLMOps: Model registries, CI/CD for AI. Experience with Langfuse, Langsmith or W&B
Cloud & Platform: AWS/Azure; containerization (Docker), orchestration (Kubernetes), serverless where appropriate.
Languages/Stacks: Proficiency in at least one of Java, Python, or Node.js; familiarity with modern frameworks and API design.
Agent Frameworks: Experience with at least one: Langchain, Strands Agents
Orchestration: , Langgraph, Temporal
Core Competencies
Systems thinking with strong abstraction and decomposition skills.
Pragmatic decision-making balancing speed, safety, and cost.
Excellent written and verbal communication with executive presence.
Strong influence without authority; facilitates alignment across functions.
Continuous learning and bias for action; outcome-oriented mindset.
Nice to Have
Background in cost optimization for AI workloads (token efficiency, quantization, caching strategies).
Prior work with model risk management, safety testing, and red-teaming.
Domain Driven Design
MLOps, classical machine learning, ML Flow, AWS Sagemaker
Experience with AWS Agent Core
Datawarehouse, Data Lake, preferably Snowflake
Impact Measures
Architectural integrity and reuse across product lines.
Reduction in time-to-market via reference patterns and platform capabilities.
Security, privacy, and compliance adherence (audit readiness, incident rates).
Uplift of engineering practices and cross-team productivity.
Career Stage:
ManagerLondon Stock Exchange Group (LSEG) Information:
Join us and be part of a team that values innovation, quality, and continuous improvement. If you're ready to take your career to the next level and make a significant impact, we'd love to hear from you.
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