AI Transformation Engineer

Dialpad · Bengaluru, Indiamid

En corto

▸Ingeniero de IA que construye sistemas productivos con LLMs y agentes
▸Trabaja en workflows de IA, RAG, automatización y APIs en entornos de producción
▸Destaca por el impacto directo en experiencias de clientes con IA autónoma y contexto compartido

Fluency in English required for collaboration with global teams

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

🎧¿Llegás a la entrevista? Llevá el copiloto. Nuestra extensión escucha la entrevista en vivo y te muestra anclas de 3-4 palabras desde tu CV y tu preparación — mirás, conectás, hablás. Gratis. Ver la extensión →

¿Qué piden?

✓Experiencia en Python para desarrollo backend
✓Conocimiento práctico de LLMs y frameworks de agentes
✓Experiencia en construcción de sistemas de RAG
✓Capacidad para arquitectar servicios escalables y APIs
✓Habilidades en evaluación y observabilidad de sistemas de IA
✓Disponibilidad para overlap con horarios de IST en mañana

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonLLMsAgent frameworksRAGAPIsBackend servicesEvaluation pipelinesRetrieval systemsObservabilityProduction-grade systems

¿A quién escribirle en Dialpad?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

About Dialpad Dialpad is the AI platform for customer experience, built to resolve customer problems in real time across voice and digital. Our AI agents learn from your best human agents and improve with every interaction, helping organizations understand their customers, deliver better experiences, increase operational efficiencies, and build a lasting competitive advantage. Unlike legacy systems built to route and answer, or standalone agentic bot vendors built to deflect, Dialpad was built to resolve. Our AI agents and human agents operate on a single platform with shared context, allowing Agentic AI to resolve issues, advance deals, and eliminate busywork through automation while seamlessly handing conversations to humans when needed, with full context preserved. Market-leading brands, including Randstad, Motorola Solutions, Netflix, the San Diego Padres, the Colorado Rockies Baseball Club, and Cal Athletics, trust Dialpad. Dialpad is backed by Andreessen Horowitz, GV, ICONIQ Capital, and T-Mobile. Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic . Your role As an AI Engineer, the selected candidate will build and ship AI-powered tools alongside a small, technically focused team. This is a hands-on engineering role: the candidate will write Python, work with LLMs and agent frameworks, integrate APIs, and help deploy systems that real business teams depend on. Guidance will be provided by senior engineers and the AI Engineering Manager, and the successful candidate is expected to grow quickly — both in AI engineering depth and in the ability to take on problems with increasing independence over time. This role is based in India and reports to the AI Engineering Manager. The candidate must be available for overlap with the core team during morning IST hours to support collaboration and code reviews across time zones. What you’ll 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,

No apliques sin prepararte

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