Application Architect

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:

London 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.

LSEG is a leading global financial markets infrastructure and data provider. Our purpose is driving financial stability, empowering economies and enabling customers to create sustainable growth.

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