Overview

Systems Architect (Staff Architect II), TSG Software Engineering

Responsibilities

• Define and maintain a unified software architecture approach across all teams, including reference architectures, design patterns, integration and API standards, and architecture decision records.
• Act as design authority for significant new systems and major upgrades, reviewing solution designs for fit with standards, scalability, resilience, security, and cost.
• Define NFRs such as performance, availability, scalability, security, observability, and maintainability, and make sure they are built into designs, estimates, and development practices.
• Guide data architecture choices across SQL, NoSQL, and vector databases so teams make consistent, well-reasoned decisions.
• Advise on the modernization, rationalization, or retirement of legacy applications, working with AMS leads and Engineering Managers on technical roadmaps and technical debt.
• Ensure vendor-augmented teams follow the same architecture and engineering standards as internal teams.
• Own the NFR system, templates, and process by which NFRs are captured, validated, tracked, and verified for every product.
• Own the inventory of all applications and legacy systems, and keep it current and complete, covering technology stack, ownership, dependencies, hosting, and lifecycle status.
• Drive up-to-date documentation of software systems and processes, and set documentation standards for teams.
• Define and own secure software development practices across teams, including threat modeling, secure design review, dependency and supply chain security, secrets management, and code scanning.
• Work with InfoSec to turn policy into practical engineering controls and serve as the technical escalation point for security questions.
• Set controls for the secure use of AI-generated code and AI agents, including protection of data and prompts.
• Review vulnerability and remediation trends across teams and drive corrective action.
• Define standards for the use of code assistants and AI coding agents, including approved tools, usage practices, review of AI-generated code, and how impact is measured.
• Design and maintain guardrails for AI agents, including permissions, handling of data and prompts, human review checkpoints, auditability, and failure handling.
• Promote AI-driven development lifecycle (AIDLC) practices and their adoption across teams.
• Define architecture patterns for LLM-based applications, including retrieval-augmented generation (RAG), vectorization and embeddings, vector databases, evaluation, cost, and latency.
• Track emerging AI risks and ensure approaches align with Bain's governance and responsible AI expectations.
• Finalize and govern deployment approaches across Azure, GCP, and AWS, including criteria for workload placement, environment strategy, and infrastructure as code.
• Standardize CI/CD pipelines, release strategies, and observability practices across teams.
• Partner with infrastructure and cloud teams on cost, resilience, and disaster recovery.
• Verify that NFRs are met in deployed environments.
• Make and recommend tool choices across IDEs, code assistants, testing, CI/CD, security, and observability, with clear evaluation criteria and rationale as and when there is a decision point.
• Manage tool licenses for all teams, internal and vendor-augmented, and optimize usage and cost.
• Lead procurement of tools, including requirements, vendor evaluation, commercial input, and onboarding, with procurement and reporting to the TS leadership.
• Lead or sponsor proof of concept on emerging technologies for different teams, with clear success criteria and a recommendation to adopt, defer, or drop. Present findings to the wider engineering group.
• Act as technical evangelist across teams through communities of practice, tech talks, and architecture forums, building shared understanding of standards and the reasons behind them.
• Communicate architecture decisions clearly to technical and non-technical audiences, including senior stakeholders.
• Coach and mentor developers and Engineering Managers on design, architecture, and technical decision-making to build architecture capability across the group.
• Report to the TS leadership and give Engineering Managers regular feedback on the technical health of teams.

Qualifications

• 15+ years in software engineering, including 5+ years in architecture or senior technical leadership roles spanning multiple systems and teams.
• Demonstrated record as architect or design authority for enterprise-scale applications, setting standards that were adopted across several teams.
• Experience working with distributed teams that combine internal and vendor-augmented developers.
• Hands-on experience with AI-assisted development and LLM-based applications, and the ability to define practical guardrails for AI agents.
• Strong grounding in secure software development practices.
• Experience designing and deploying on at least one major cloud platform (Azure, GCP, or AWS), with working knowledge of the others.
• Experience evaluating tools, managing licenses, supporting procurement, and running proofs of concept.
• Ability to influence and lead without direct authority, and to earn credibility with engineers and senior stakeholders alike.
• Strong communication, listening, and customer service skills.
• Strong analytic and problem-solving skills.
• Bachelor's or equivalent degree.
• Architecture: scalable, secure, and maintainable system design, including microservices, event-driven patterns, API design, integration patterns, and NFR definition.
• Backend languages and frameworks: Python, C#, .NET, and .NET Core.
• Web frameworks and libraries: Angular, React, Next.js, and TypeScript.
• AI and Gen AI: LLMs, ML, MLOps/ AIOps, prompt design, RAG, AI agents, AI coding assistants, agent guardrails, and evaluation.
• Vector databases and vectorization: embedding models, vector stores, indexing, and retrieval tuning.
• SQL databases: Microsoft SQL Server, Databricks, PostgreSQL, or equivalent.
• NoSQL databases: MongoDB, Cosmos DB, or equivalent.
• Cloud: Azure, GCP, and AWS, including infrastructure as code and workload placement.
• DevOps: CI/CD pipelines, release management, containers, and observability.
• Testing: Exposure to unit testing, API and end-to-end testing, and performance testing tools.
• Secure development: threat modeling, SAST and DAST, dependency scanning, and secrets management.
• Architecture documentation: C4 or UML modeling and architecture decision records.