Design end-to-end solutions covering domain models, business services, APIs, user interfaces, persistence, integrations, security, and deployment.
Establish standards for modular application design, service boundaries, API contracts, error handling, configuration, and versioning.
Review major application designs for functional suitability, scalability, reliability, security, maintainability, and operational supportability.
Guide teams in selecting appropriate architectural styles, including modular monoliths, microservices, event-driven components, and workflow-based solutions.
Identify architectural risks, design inconsistencies, and technical debt, and drive practical remediation plans.
Remain hands-on through prototypes, proof-of-concepts, reference implementations, and targeted development in uncertain or high-risk areas.
AI-Assisted Software Engineering
Define guidelines for the responsible and effective use of AI coding assistants and generative AI tools across the software development lifecycle.
Establish recommended practices for AI-assisted requirements analysis, architecture, coding, testing, documentation, code review, and troubleshooting.
Define quality gates and review expectations for AI-generated or AI-assisted code.
Develop reusable prompts, templates, coding patterns, architectural context, and development workflows that improve consistency and engineering productivity.
Guide teams in using AI to generate and maintain unit tests, API tests, UI tests, documentation, migration scripts, and technical designs.
Define guardrails for security, privacy, intellectual property, licensing, data handling, and human oversight when using AI tools.
Evaluate emerging AI development tools and recommend their adoption where they provide measurable engineering value.
Measure the impact of AI-assisted development on delivery speed, quality, test coverage, maintainability, and developer experience.
Engineering and Quality Standards
Set standards for architecture, APIs, authentication and authorization, observability, testing, CI/CD, configuration, and deployment.
Establish expectations for automated unit, integration, API, contract, and end-to-end testing.
Partner with QA and engineering teams to improve testability, regression coverage, and production confidence.
Promote design-for-quality practices, including validation, resilience, diagnosability, performance, and backward compatibility.
Run architecture reviews, critical code reviews, and technical design discussions.
Define and promote engineering practices that reduce defects and prevent the accumulation of technical debt.
Technical Leadership
Drive technical alignment across teams and resolve cross-team dependencies.
Communicate architectural decisions, trade-offs, constraints, and risks to both technical and non-technical stakeholders.
Mentor engineers and technical leads in application design, cloud architecture, software quality, and AI-assisted development.
Collaborate with product management to assess technical feasibility, delivery risks, and sequencing of major initiatives.
Escalate significant technical, security, reliability, and operational risks.
Maintain practical architecture documentation, decision records, standards, and reference implementations.
REQUIRED SKILLS AND EXPERIENCE
12-15 years of experience in enterprise software engineering, with significant experience in application architecture and technical leadership.
Strong experience designing end-to-end business applications and platforms.
Strong understanding of domain modeling, modular design, service boundaries, API-first development, integration patterns, and distributed systems.
Hands-on backend development experience with Python/Django/DRF and/or Go/Gin.
Strong frontend architecture experience with Angular and TypeScript; working knowledge of React is a plus.
Experience designing relational data models, indexes, queries, and persistence strategies using MySQL and PostgreSQL.
Experience with Redis for caching, distributed coordination, or queue-based processing.
Strong understanding of REST APIs, API versioning, asynchronous processing, event-driven systems, and workflow orchestration.
Experience with authentication and authorization standards, including OAuth 2.0, OpenID Connect, JWT, RBAC, and secure secret management.
Strong Azure experience, including Container Apps, Functions, Key Vault, storage, networking, identity, and monitoring.
Experience with Docker, Terraform, Azure DevOps, and CI/CD automation.
Experience with observability, logging, metrics, tracing, performance analysis, and production troubleshooting.
Demonstrated ability to use or establish AI-assisted software development practices, including code generation, test generation, design assistance, and documentation workflows.
Ability to assess AI-generated output critically and apply appropriate human review and quality controls.
PREFERRED SKILLS
Experience building shared engineering platforms, frameworks, libraries, or developer-productivity tooling used by multiple teams.
Experience defining engineering playbooks, architecture standards, coding guidelines, or development governance.
Experience implementing AI-enabled developer workflows or internal engineering assistants.
Experience with agentic AI, retrieval-augmented generation, tool-calling agents, or enterprise AI application patterns.
Experience with event-driven architecture, message queues, and workflow orchestration.
Experience with Playwright or other modern UI automation frameworks.
Experience working closely with product, QA, DevOps, security, and enterprise architecture teams.
Experience modernizing legacy applications incrementally while continuing feature delivery.
SOFT SKILLS
Strong architectural thinking and practical application-design judgment.
Ability to reason clearly about trade-offs, constraints, risks, and long-term maintainability.
Clear written and verbal communication with technical and non-technical stakeholders.
Ability to influence and build alignment without direct reporting authority.
Strong ownership mindset and willingness to work hands-on when necessary.
Curiosity and adaptability in evaluating new technologies and AI development approaches.
Commitment to engineering quality, security, responsible AI use, and continuous improvement.
EDUCATIONAL QUALIFICATION
Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
Equivalent practical experience with a strong record of technical leadership may also be considered.
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