Position Purpose: We are seeking a deeply technical Engineering Manager to lead a Client Onboarding engineering team in Colombia. This is a player-coach role, not a traditional management-only position. The successful candidate will set the technical direction for the team, lead architecture and solution design, guide engineers through complex delivery challenges, and remain directly involved in the codebase.
Approximately 70% of the role will focus on technical leadership, architecture, system design, AI enablement, engineering standards, mentoring, and delivery direction. The remaining 30% will involve hands-on individual contribution, including development of critical functionality, proof-of-concept implementation, code reviews, debugging, and production problem-solving.
The Engineering Manager will also lead the team’s adoption of AI-assisted and agentic engineering practices. This includes the effective use of enterprise-approved AI coding assistants, task-specific engineering agents, automated testing agents, reusable skills and workflows, and AI-supported modernization approaches.
AI fluency is an explicit expectation of this role. The Engineering Manager should be able to distinguish between using AI to improve software delivery and embedding AI capabilities into the products and platforms the team develops.
Primary duties will include as per below:
Technical Leadership, Architecture, and Team Direction — Approximately 70%
- Own the technical direction and solution architecture for the engineering team, ensuring alignment with enterprise standards and the broader Client Onboarding technology strategy.
- Lead end-to-end system design across React applications, APIs, microservices, workflow components, relational and NoSQL databases, event streams, cloud infrastructure, and external integrations.
- Translate complex business, operational, and regulatory requirements into scalable technical designs and executable engineering plans.
- Produce and review architecture diagrams, technical specifications, API contracts, data models, event schemas, integration patterns, and implementation approaches.
- Drive modernization initiatives involving cloud-native applications, microservices, event-driven architecture, containerization, platform engineering, and legacy application transformation.
- Act as the team’s primary technical escalation point for architecture, design, development, integration, performance, security, and production issues.
- Lead technical design reviews and code reviews while ensuring key architectural decisions are documented and understood by the team.
- Establish and reinforce engineering standards covering code quality, automated testing, CI/CD, security, observability, resilience, performance, and maintainability.
- Ensure non-functional requirements—including scalability, availability, recoverability, security, auditability, and monitoring—are incorporated into solution designs from the outset.
- Identify and mitigate architectural risks, technical debt, delivery bottlenecks, system inefficiencies, and cross-team dependencies.
- Break complex initiatives into clear technical deliverables and provide engineering estimates, implementation sequencing, dependency analysis, and delivery guidance.
- Mentor developers and technical leads, helping them strengthen their architecture, system design, coding, testing, troubleshooting, and production-support capabilities.
- Promote a culture of technical ownership, experimentation, continuous improvement, knowledge sharing, and engineering excellence.
AI and Agentic Engineering Leadership
- Define and drive the team’s strategy for using AI across the software-development lifecycle, including requirements analysis, solution design, coding, testing, documentation, code review, modernization, debugging, and incident investigation.
- Promote the responsible use of enterprise-approved AI development tools such as GitHub Copilot, Claude Code, or equivalent platforms.
- Identify opportunities to use agentic AI to automate repeatable engineering activities, including:
- Code generation and refactoring.
- Legacy-code analysis and modernization.
- Unit, integration, and end-to-end test creation.
- Pull-request analysis and code-quality checks.
- API and technical-documentation generation.
- Dependency and vulnerability analysis.
- Production-log investigation and root-cause analysis.
- Application onboarding, configuration, and repository setup.
- Design reusable AI engineering assets, including prompts, instructions, skills, rules, agent definitions, workflows, reference implementations, test suites, and evaluation criteria.
- Establish a centrally governed approach for distributing approved AI guardrails and reusable agent capabilities across engineering repositories.
- Lead proof-of-concept initiatives to validate AI-assisted development approaches before broader adoption.
- Evaluate where agentic workflows provide measurable value and where deterministic software, traditional automation, or human review remains more appropriate.
- Establish human-in-the-loop controls for AI-generated code, designs, tests, documentation, and production recommendations.
- Ensure AI-assisted engineering complies with enterprise requirements for security, data privacy, intellectual property, access control, auditability, and regulatory compliance.
- Define standards for reviewing and validating AI-generated output, including automated testing, security scanning, peer review, traceability, and approval requirements.
- Measure the impact of AI adoption through tangible engineering outcomes such as delivery cycle time, development effort, test coverage, defect rates, rework, maintainability, and production stability.
- Evaluate new models, agent frameworks, developer tools, orchestration platforms, and AI engineering patterns while avoiding unnecessary vendor lock-in.
Product and Platform AI Capabilities
- Identify appropriate opportunities to embed AI or machine-learning capabilities within Client Onboarding products and internal platforms.
- Guide the architecture of AI-enabled solutions using relevant patterns such as large language models, retrieval-augmented generation, vector search, structured output, tool or function calling, and workflow orchestration.
- Ensure AI-enabled product capabilities include appropriate safeguards for accuracy, explainability, auditability, data protection, and human oversight.
- Define evaluation approaches for AI capabilities, including expected behavior, accuracy thresholds, failure conditions, regression testing, and operational monitoring.
- Partner with Product, Architecture, Information Security, Compliance, and Data teams when evaluating or implementing AI-enabled functionality.
Engineering and Delivery Leadership
- Partner with Product and Quality Engineering to ensure requirements, acceptance criteria, testing strategies, and implementation plans are aligned before development begins.
- Coordinate with Architecture, Cloud Engineering, DevOps, Information Security, and other engineering teams to resolve dependencies and deliver integrated solutions.
- Maintain a strong focus on delivery while balancing strategic architecture, platform modernization, technical debt, AI experimentation, and operational responsibilities.
- Provide appropriate people leadership, including setting expectations, providing technical feedback, supporting career development, contributing to hiring, and addressing performance concerns.
Hands-On Individual Contribution — Approximately 30%
- Design, develop, test, and maintain production-grade functionality using React, TypeScript, C#, and .NET Core.
- Contribute directly to critical code paths, shared frameworks, complex integrations, and technically challenging features.
- Create reference implementations that demonstrate the expected architecture, coding standards, testing practices, and AI-assisted development approach.
- Personally use approved AI development tools to accelerate implementation while maintaining accountability for the quality and correctness of the final output.
- Build and refine task-specific engineering agents, reusable prompts, agent workflows, repository instructions, and development guardrails.
- Develop proofs of concept for agentic engineering workflows and AI-enabled product capabilities.
- Experiment with patterns such as tool calling, structured outputs, retrieval-augmented generation, AI-assisted testing, and workflow orchestration.
- Troubleshoot complex application, infrastructure, data, integration, and production issues, working with engineers to identify root causes and implement sustainable fixes.
- Participate actively in code reviews and provide detailed, actionable feedback on architecture, design, security, performance, testability, and maintainability.
- Review AI-generated code and technical artifacts with the same rigor applied to manually produced work.
- Build and maintain automated tests, including unit, integration, contract, component, performance, and end-to-end tests.
- Contribute to CI/CD pipelines, deployment automation, application telemetry, dashboards, alerts, and operational-readiness activities.
- Remain close to the codebase and delivery process to maintain technical credibility and make informed engineering decisions.