Application Architect

LSEG; Taguig; On-site; PHP 1,500,000 - 2,000,000; Full time

Benefits offered by this job

Healthcare

Retirement planning

Paid volunteering days

Wellbeing initiatives

Job summary

A leading technology firm is seeking an experienced AI Application Architect to define and oversee the architecture of enterprise-grade AI applications. The role involves hands-on solution design and leadership in AI applications, requiring 10+ years in software architecture and relevant experience in AI/LLM solutions. Candidates should be proficient in cloud technologies, possess strong communication skills, and have a track record in secure, scalable applications. Benefits include healthcare, retirement planning, and paid volunteering days.

Qualifications

• Proven track record designing secure, scalable, compliant enterprise applications.
• Experience guiding multi-team programs and influencing senior stakeholders.
• Architect production LLM solutions using RAG and embedding techniques.

Responsibilities

• Define target-state architecture for AI-enabled applications.
• Establish integration patterns for LLM invocation.
• Coach teams on AI application patterns and platform reuse.

Skills

Distributed systems

Microservices

APIs

Event-driven patterns

Streaming

Resiliency

Caching

Prompt engineering

Model deployment

Education

10+ years in software/application architecture or senior engineering roles

2–4+ years hands-on with AI/LLM solution architecture

Tools

AWS

Azure

Docker

Kubernetes

Job description

AI Application Architect

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‑team, 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‑team.
• 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: Manager

We are proud to be an equal opportunities employer. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, gender identity, gender expression, age, marital status, veteran status, pregnancy or disability, or any other basis protected under applicable law. We can reasonably accommodate applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs.

Benefits include healthcare, retirement planning, paid volunteering days, and wellbeing initiatives. LSEG offers a range of tailored benefits and support.