AI Engineer

Snapp!; General Trias; Presencial; PHP 900.000 - 2.100.000; Jornada completa

Descripción de la vacante

Snapp! is seeking an AI Engineer to build scalable AI solutions across agents, search, and foundation-model-based apps.

You will own projects end-to-end from research to deployment, collaborating with product and engineering teams to translate business needs into concrete technical plans. You will design and deploy LLM-powered applications, explore prompt engineering and RAG, and optimize performance, latency, and costs while ensuring robust data privacy and reliability.

Formación

• Bachelor's degree or higher in Computer Science, Computer Engineering, Artificial Intelligence, or related field.
• At least 3 years of experience in AI engineering, ML, or software engineering focused on AI-powered products.
• Hands-on experience building and deploying ML or foundation-model-based solutions.

Responsabilidades

• Collaborate with product and engineering teams to translate business needs into technical plans.
• Design, build, and deploy LLM-powered applications and AI agents.
• Select and adapt foundation models using prompt engineering, RAG, and fine-tuning.
• Contribute to semantic search, ranking, and speech initiatives.
• Create evaluation datasets and experiments to measure model quality.
• Write production-quality code and participate in deployment and monitoring.
• Optimize model quality, latency, and resource use.
• Document decisions and share best practices.

Conocimientos

Python

Software engineering

API integration

Testing

Linux

Docker

PyTorch

Problem solving

Educación

Bachelor's degree or higher in CS/CE/AI

Herramientas

PyTorch

Docker

Linux

Descripción del empleo

Job Description:

As an AI Engineer, you will build scalable AI solutions that improve user experiences, automate operations, and address real business needs. Our work spans Agentic AI, intelligent search, speech processing, and foundation-model-based applications. You will take ownership of AI projects end-to-end, from research and prototyping to evaluation, deployment, and continuous improvement.

• Collaborate with product, engineering, and operations teams to translate business needs into well-defined technical problems, measurable success criteria, and actionable implementation plans.

• Design, build, and deploy LLM-powered applications and AI agents, including conversational workflows, tool and API integrations, retrieval mechanisms, state management, and error recovery.

• Select, evaluate, and adapt foundation models using appropriate techniques such as prompt engineering, retrieval-augmented generation (RAG), and fine-tuning.

• Contribute to semantic and hybrid search, ranking, reranking, and speech-related initiatives based on team and business priorities.

• Design evaluation datasets and reproducible experiments to measure model quality and end-to-end system performance.

• Investigate system failures, analyze error patterns, and identify opportunities for improvement across data, models, prompts, tools, and system architecture.

• Write clean, maintainable, production-quality code and contribute to deployment, monitoring, troubleshooting, and ongoing system maintenance.

• Optimize model quality, latency, inference costs, and resource utilization within product and infrastructure constraints.

• Document technical decisions, participate in code reviews, and share knowledge and best practices across the team.

• Implement appropriate safeguards for data privacy, access control, and AI agent actions.

• Bachelor's degree or higher in Computer Science, Computer Engineering, Artificial Intelligence, or a related technical field.

• At least 3 years of relevant professional experience in AI engineering, machine learning, or software engineering focused on AI-powered products.

• Hands-on experience building and deploying at least one machine learning or foundation-model-based solution in a real-world environment.

• Strong Python programming skills and solid software engineering fundamentals, including modular design, version control, testing, debugging, and API integration.

• Good understanding of machine learning and deep learning fundamentals, including Transformers, embeddings, and model evaluation.

• Practical experience working with large language models (LLMs) and a solid understanding of RAG, tool calling, and agentic workflows.

• Ability to build evaluation datasets, analyze errors, and distinguish between data-related, model-related, and system-level issues.

• Experience with a major deep learning framework, such as PyTorch, and tools for working with pretrained models.

• Familiarity with Linux, Docker, and the fundamentals of service deployment and monitoring.

• Ability to read technical documentation and research papers in English and apply relevant findings to practical engineering problems.

• Strong problem-solving, analytical thinking, independent learning, communication, and collaboration skills.

• A strong sense of ownership and the ability to navigate ambiguity, make informed technical decisions, and deliver results.

Experience in one or more of the following areas is a plus:

• Building production-ready AI agents with multiple tools, state management, and robust failure recovery.

• Working with open-source or open-weight models, fine-tuning, model serving, and inference optimization.

• Developing search, retrieval, ranking, reranking, or recommendation systems.

• Working with automatic speech recognition (ASR), text-to-speech (TTS), audio processing, or real-time conversational AI systems.

• Working with Persian-language data and addressing language-specific modeling and evaluation challenges.

• Building high-traffic services, asynchronous processing pipelines, or systems running on GPU infrastructure.