Why This Job is Featured on The SaaS Jobs
This AI Transformation Analyst role stands out as an internal-product build within a SaaS company, focused on applying LLMs to operational workflows rather than shipping a customer-facing feature set. The remit spans multi-agent systems, RAG, and automation, signalling a pragmatic “AI-in-the-business” approach where reliability, evaluation, and integration with existing systems matter as much as model selection.
For a SaaS career, the durable value here is end-to-end ownership of production AI services: turning ambiguous stakeholder needs into deployed systems with monitoring, testing, and iteration loops. That combination of applied ML engineering and platform-minded software engineering maps well to how modern SaaS teams adopt AI, where success is measured in uptime, latency, quality, and maintainability. Experience defining reusable components and engineering standards also translates across organisations building shared AI capabilities.
This position is best suited to a senior individual contributor who prefers leading implementation and influencing technical direction through architecture and mentorship. It fits someone comfortable operating across engineering and business teams, and who enjoys building guardrails and evaluation frameworks that keep LLM applications dependable in production. The Argentina-based setup with required overlap with North American teams will appeal to professionals who can collaborate across time zones with clear written communication.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Your role
As an AI Transformation Analyst (P3), you will design, build, and own AI powered products that improve how Dialpad's internal teams operate. You'll work across the entire lifecycle, from identifying opportunities with stakeholders, architecting scalable AI systems, and deploying production ready solutions, to monitoring and continuously improving their performance.
You'll be expected to independently lead complex engineering initiatives, influence technical direction, and mentor junior engineers while collaborating closely with product, analytics, and business teams.
This role is based in Argentina and reports to the Senior Manager, AI Transformation. The candidate should maintain overlap with North American and global engineering teams to support technical collaboration, architecture discussions, and project delivery.
What You Will Do
- Design, build, and maintain production grade AI systems, including multi-agent workflows, RAG applications, workflow automation, and LLM-powered services.
- Lead the technical implementation of complex AI initiatives from discovery through deployment.
- Architect scalable backend services, APIs, evaluation pipelines, and retrieval systems.
- Improve reliability, observability, and performance of production AI applications.
- Define engineering standards, reusable components, and best practices for AI development.
- Collaborate directly with business stakeholders to understand problems and translate them into scalable AI solutions.
- Evaluate new foundation models, frameworks, and prompting strategies, making recommendations for adoption.
- Build evaluation frameworks and automated testing strategies for LLM-based systems.
- Mentor junior engineers through code reviews, technical guidance, and design discussions.
- Participate in architectural planning, sprint planning, technical reviews, and cross-functional initiatives.
- Drive continuous improvements in engineering processes, documentation, and operational excellence.
Skills You Will Bring
- 5+ years of experience in software engineering, machine learning engineering, AI engineering, or a similar technical role.
- Strong Python development skills with experience building and maintaining production systems.
- Deep understanding of LLMs, prompting techniques, structured outputs, tool calling, function calling, and AI application architectures.
- Experience designing and deploying RAG systems, including chunking strategies, embeddings, retrieval optimization, reranking, and evaluation.
- Experience building agentic workflows using frameworks such as LangGraph, LangChain,, OpenAI Agents SDK, or similar.
- Experience integrating external APIs and enterprise systems.
- Strong understanding of software engineering best practices including testing, CI/CD, observability, monitoring, and production deployments.
- Experience working with cloud platforms (preferably GCP) and containerized applications.
- Strong debugging and problem-solving skills.
- Excellent written and verbal English communication.
- Ability to independently manage projects and influence technical decisions across teams.
Preferred Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, AI, or a related field.
- Experience deploying AI systems into production at scale.
- Experience with vector databases such as Vertex AI Vector Search, or pgvector.
- Experience building evaluation frameworks for LLM quality, latency, and cost optimization.
- Experience with orchestration platforms such as n8n, LangGraph, Airflow, or similar.
- SQL and BigQuery experience.
- Contributions to open-source AI projects or notable personal AI applications.
