VPP-123 - AI Engineering Lead / AI SDLC Architect

VPP-123 – AI Engineering Lead / AI SDLC Architect

Department:

AI, Insights, & Solutions

Project Location(s):

Brazil, Mexico, Argentina - Remote

Job Type:

Employee

Education:

Bachelor's

Who You’ll Work With

We are seeking a hands-on AI Engineering Lead / AI SDLC Architect who will work closely with engineering teams, architecture teams, AI specialists, and various stakeholders to drive the adoption of AI technologies in the software development lifecycle within a large enterprise enviroment. Collaboration with EQE and other engineering teams is essential to establish effective practices and standards.

The ideal candidate combines strong .NET/C# software engineering and enterprise architecture experience with hands-on Generative AI expertise, excellent communication skills, and the ability to drive AI adoption across multiple development teams.

What You’ll Do

Roles and responsibilities:

AI-Driven Software Development Lifecycle:

Lead the architecture, design, and implementation of AI-enabled software development capabilities across the SDLC.
Develop solutions that leverage AI to transform business requirements into user stories, technical specifications, architecture artifacts, development tasks, and implementation guidance.
Design AI-assisted workflows supporting code generation, unit testing, regression testing, code quality, validation, and deployment.
Identify opportunities to automate and improve engineering processes while maintaining quality, security, governance, and maintainability.
Establish scalable AI-SDLC standards, implementation patterns, and reusable development practices.

Enterprise Context Engineering and Knowledge Architecture:

Design and implement enterprise knowledge architectures that enable AI systems to understand business processes, application responsibilities, system dependencies, and technical boundaries.
Build and enhance Retrieval-Augmented Generation (RAG) capabilities, including document ingestion, knowledge retrieval, context management, integration, testing, and production readiness.
Develop context-engineering strategies that allow AI agents to retrieve and interpret information from multiple repositories, applications, and enterprise systems.
Design solutions that determine which systems, data sources, APIs, and repositories are relevant to specific business requirements.
Ensure AI-generated requirements and technical artifacts accurately reflect the organization’s existing architecture and engineering standards.

AI Agents, Coding Harnesses, and Reusable Frameworks:

Design and implement AI agents, agentic workflows, coding harnesses, and AI-assisted development frameworks.
Build reusable AI components, integrations, and development tools that engineering teams can incorporate into their daily workflows.
Develop modular frameworks, repository-level instructions, Markdown context files, architectural guidance, and reusable AI development patterns.
Create plug-and-play capabilities that can be adopted consistently across multiple engineering teams.
Develop hands-on prototypes, proof-of-concepts, and production-ready components to validate proposed AI solutions.
Evaluate emerging AI development tools and approaches, recommending appropriate technologies and architectural patterns.

Enterprise Software Architecture and Integration:

Work within a complex enterprise application ecosystem that includes .NET/C# services, Angular single-page applications, microservices, APIs, shared libraries, and multiple independent code repositories.
Design AI capabilities that integrate effectively with modern cloud applications and existing legacy enterprise systems, including mainframe-based systems of record.
Define integration patterns, reusable architectural components, and CI/CD approaches that support enterprise-scale AI-assisted development.
Ensure proposed solutions account for application dependencies, security requirements, existing technology standards, and system boundaries.
Collaborate with enterprise architects to review, validate, and align technical designs before implementation.

Engineering Quality, Testing, and Production Readiness:

Partner with engineering, QA/EQE, platform, and architecture teams to establish appropriate testing and validation practices for AI-generated artifacts and AI-enabled workflows.
Define approaches to evaluate the accuracy, reliability, security, and maintainability of AI-generated outputs.
Support AI solution integration, deployment, monitoring, and production readiness.
Ensure reusable AI development capabilities can scale across teams without compromising engineering quality or operational stability.

Technical Leadership and Engineering Enablement:

Serve as a hands-on technical leader and close partner to lead developers, software architects, and engineering teams.
Demonstrate AI-driven development solutions through working prototypes, technical workshops, and practical implementation examples.
Educate developers on AI-assisted engineering tools, architectural patterns, coding harnesses, and effective AI-SDLC practices.
Drive adoption of reusable AI development capabilities across multiple teams.
Collaborate with enterprise architects, engineering leaders, AI specialists, and platform teams to establish technical direction and implementation priorities.
Facilitate architectural discussions, constructively challenge technical proposals, and influence stakeholders toward effective engineering solutions.
Navigate competing priorities, evolving requirements, and technical disagreements with professionalism and flexibility.
Take ownership of technical problems even when the implementation approach is not fully defined, bringing together the necessary teams or building the solution directly when appropriate.

The role is not focused primarily on developing standalone chatbots or customer-facing AI applications. Its purpose is to build the technical architecture, frameworks, and engineering capabilities that enable enterprise development teams to adopt AI throughout the software development lifecycle.

What You’ll Bring

Software Engineering, .NET and Angular Background

Strong professional background in software engineering, with extensive hands-on experience designing, developing, and integrating enterprise software applications.
Strong hands-on .NET/C# development experience is mandatory, including experience building and integrating enterprise applications and backend services.
Hands-on Angular experience is mandatory, including developing or integrating enterprise web applications with backend services.
Proven experience with enterprise software architecture, application integration, APIs, microservices, and distributed systems.
Strong understanding of the complete Software Development Lifecycle (SDLC), including requirements analysis, architecture, coding, testing, integration, deployment, and maintenance.
Experience designing enterprise-scale solutions, reusable frameworks, integration patterns, and CI/CD pipelines.
Ability to remain technically hands-on, building prototypes, integrations, reusable components, and production-ready solutions.
Experience working with complex application environments involving multiple repositories, services, dependencies, and development teams.

Generative AI and AI-Assisted Software Development

Strong hands-on experience designing and implementing Generative AI solutions using LLMs and related technologies.
Demonstrated experience incorporating AI into software engineering workflows and developer productivity initiatives.
Strong understanding of AI-enabled SDLC practices, including the use of AI for requirements engineering, code generation, testing, code quality, and delivery.
Experience designing or implementing coding harnesses, AI development frameworks, reusable tooling, or equivalent solutions that help developers incorporate AI into their daily work.
Practical experience implementing Retrieval-Augmented Generation (RAG) solutions end to end, including architecture, retrieval, integration, testing, and deployment.
Experience designing and implementing AI agents and agentic workflows.
Ability to determine how AI should process business or technical requirements, what enterprise context is required, which systems must be accessed, and how the complete workflow should be orchestrated.
Experience designing reusable AI capabilities that can be adopted across multiple engineering teams.

Enterprise Architecture and Context Engineering

Strong understanding of enterprise software architecture, including application dependencies, system integration, data flows, and architectural boundaries.
Ability to design AI knowledge retrieval and context-management solutions that support complex enterprise application environments.
Experience translating business requirements into user stories, technical designs, development tasks, and executable implementation plans.
Experience defining architectural standards, reusable engineering patterns, and scalable technical solutions.
Ability to work effectively with enterprise architecture governance processes and technical design reviews.

Quality, Security, and Production Readiness

Strong understanding of software testing, quality assurance, CI/CD, security, governance, and production readiness.
Experience integrating AI solutions into established enterprise engineering and delivery processes.
Ability to evaluate the accuracy, reliability, and maintainability of AI-generated development artifacts.
Experience collaborating with QA/EQE, platform, architecture, and engineering teams to ensure appropriate technical validation and quality standards.

Technical Leadership and Communication

Proven ability to lead technical initiatives across multiple engineering teams while remaining hands-on.
Strong architectural decision-making and technical problem-solving skills.
Ability to explain complex technical concepts, demonstrate solutions, and guide developers in adopting new engineering practices.
Experience collaborating with software engineers, enterprise architects, AI specialists, platform teams, and business stakeholders.
Ability to influence technical decisions, facilitate architectural discussions, and constructively challenge proposed solutions.
Professional maturity and resilience when managing technical disagreements, competing priorities, and evolving requirements.
Ability to operate independently in ambiguous environments, identify practical solutions, and drive initiatives from concept through implementation.
Excellent English communication skills are mandatory, both verbal and written, including the ability to lead technical discussions, explain architectural decisions, influence stakeholders, and collaborate effectively with US-based engineering teams.

Additional Important Technical Experience

Familiarity with enterprise environments involving multiple repositories, shared packages, APIs, and microservices.
Understanding of modern cloud architectures and integration with legacy enterprise systems.
Experience building internal developer platforms, reusable SDKs, coding tools, or AI-assisted engineering utilities.

The ideal candidate is a hands-on software architect or senior technical lead who combines a strong .NET/C# and Angular engineering foundation with practical Generative AI expertise, excellent English communication skills, and demonstrated experience enabling AI adoption across the software development lifecycle.

What Makes Us Great Place To Work

Vekend is a people-first organization focused on developing thoughtful products and services that create meaningful impact for our customers and communities. We are committed to fostering a collaborative, respectful, and inclusive work environment where employees are empowered to take ownership of their work, contribute ideas, and grow professionally. We strive to support flexibility and work-life balance while maintaining high standards of performance and accountability.

Benefits & Wellbeing

For all locations, the good-faith, reasonable annualized full-time compensation for this role will be determined based on competitive market data and may vary depending on geographic location, job-related knowledge, skills, experience, education, and other business considerations. Specific compensation details will be discussed during the interview process. Vekend offers a comprehensive benefits and wellness package designed to support employees’ overall well-being, financial security, and professional growth. Eligibility and coverage are subject to the terms of the applicable benefit plans and company policies. Eligible employees have access to medical, dental, and vision insurance with company contributions toward premiums, a 401(k) retirement plan with company matching, and Paid Time Off that begins accruing on the first day of employment. We also support flexible work arrangements and opportunities for professional growth. Benefits eligibility, coverage, and company contributions are subject to the terms of the applicable plan documents and may be modified at the company’s discretion.

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