Engineering Manager, AI Transformation

About the role

This role serves as the technical lead for the AI Transformation team, focusing on hands-on development and engineering oversight. You will define technical standards, mentor junior staff, and ensure the delivery of high-quality, production-ready AI systems. You will own the end-to-end technical lifecycle of AI initiatives, bridging the gap between ambitious product visions and robust, scalable implementations. This position requires a balance of deep coding contributions and strategic leadership to elevate the entire engineering function. You will be responsible for safeguarding code quality and system reliability while fostering a culture of continuous learning. Your decisions will directly shape the architectural integrity of our AI products. You will act as a key partner to multiple departments, translating ambiguous requirements into concrete engineering solutions. Ultimately, you will drive the technical vision for AI Transformation, ensuring alignment with broader business objectives.

Key facts

What you'll do

• Establish and enforce rigorous engineering standards, defining architecture choices and mandating thorough code reviews and evaluation frameworks to ensure production readiness.
• Design, build, and deploy complex AI-driven systems, including autonomous agents, Retrieval-Augmented Generation (RAG) pipelines, and sophisticated LLM-based automations that solve real business problems.
• Mentor analysts and junior engineers on AI fundamentals, advanced debugging techniques, and efficient production workflows to build a strong internal capability.
• Make critical technical tooling decisions, overseeing model selection processes and defining infrastructure patterns that balance performance, cost, and scalability.
• Write and review production-grade code directly, ensuring best practices are followed and that the codebase remains maintainable and secure.
• Convert complex and ambiguous business needs into actionable technical specifications, creating clear documentation that guides the development team.
• Partner closely with Analytics, Sales, CX, Finance, and Operations departments to build reliable AI solutions that deliver tangible value and improve operational efficiency.
• Create and maintain comprehensive AI evaluation practices, establishing benchmarks and metrics for continuous system improvement and performance tracking.
• Work in tandem with the Senior Manager of AI Transformation to prioritize the technical roadmap, aligning engineering efforts with strategic business goals.
• Proactively identify and resolve technical debt, implementing refactoring and improvements that enhance the long-term health of the codebase.
• Lead scoping and estimation sessions, providing accurate technical assessments for new projects and feature requests.
• Implement monitoring and logging strategies specifically for AI systems, ensuring visibility into model performance and system health.
• Facilitate knowledge transfer sessions, elevating the technical skills of the broader engineering organization around AI and machine learning concepts.
• Champion the adoption of MLOps principles, streamlining the deployment and lifecycle management of AI models in production environments.

Requirements

• Possess 8+ years of professional experience in software engineering, ML engineering, or AI systems development, with a proven track record of delivering complex projects.
• Have 1-3 years of hands-on management experience, leading at least one engineering team or a significant technical initiative.
• Demonstrate direct, hands-on experience deploying LLM-powered systems, including agents and RAG pipelines, into production environments with high reliability.
• Show exceptional proficiency in Python, using it to write, debug, and optimize production-grade code for backend services.
• Bring prior experience in mentoring or leading engineers and analysts, fostering their professional growth and technical development.
• Exhibit a deep understanding of AI evaluation methods and feedback loop design, using data to drive iterative improvements.
• Have substantial experience with cloud platforms, specifically GCP, including core services and AI-related offerings.
• Be comfortable with the full stack of software development, including designing APIs, building data pipelines, managing infrastructure, and setting up monitoring solutions.
• Communicate complex technical concepts and trade-offs clearly and effectively to non-technical stakeholders through strong English verbal and written skills.
• Thrive in a dynamic, fast-paced environment, demonstrating adaptability and problem-solving under pressure.
• Collaborate effectively with distributed and offshore teams, navigating time zone differences and cultural nuances to maintain project momentum.
• Be strictly available during US overlapping business hours, specifically from 9 AM to 6 PM ART, to ensure real-time collaboration and timely decision-making.
• Have a strong commitment to software craftsmanship, security, and operational excellence.
• Be comfortable working within a structured Agile/Scrum framework, participating in sprint planning, stand-ups, and retrospectives.

Practical notes

• This is a hands-on technical management position that requires equal parts coding acumen and leadership.
• Candidates who do not meet every qualification listed in the requirements but possess the required drive, curiosity, and ambition are strongly encouraged to apply.
• Dialpad is an equal-opportunity employer, welcoming applicants from diverse backgrounds and experiences.
• All work is to be performed from the designated location in Buenos Aires, Argentina.
• The engagement is for full-time employment, requiring a standard commitment of 40 hours per week.
• Applicants must be able to start within a reasonable timeframe as defined by the hiring process.
• No visa sponsorship is available or mentioned in the source material for this role.
• There is no mention of travel requirements, domestic or international, associated with this position.
• Compensation details are not specified in the provided source information.
• The successful candidate will be expected to adhere to the standard working hours outlined in the Practical notes section.
• The role demands a high degree of ownership, requiring the incumbent to proactively manage priorities and stakeholder expectations.
• Success in this position will be measured by the successful delivery of AI systems, the growth of the team, and the improvement of engineering processes.