AI Engineer

TOPPAN Ecquaria Pte Ltd; Santo Niño 1st; On-site; PHP 900,000 - 1,300,000; Full time

Job summary

TOPPAN Ecquaria Pte Ltd is seeking an AI Engineer to design and implement cutting-edge generative AI solutions for enterprise use. You will work on LLM-based systems, integrate with Azure OpenAI and Hugging Face, and build end-to-end pipelines for data processing and model deployment.

Join a team collaborating with domain experts to identify business opportunities, establish metrics, and ensure responsible AI practices.

Qualifications

• Bachelor's degree in CS/Engineering/Data Science or related field.
• Minimum 2 years of experience in Data Science and ML.
• Experience deploying LLMs, AI agents and AI solutions, and integrating and deploying open source AI models from HuggingFace.
• In-depth knowledge of machine learning, deep learning, and generative AI techniques.
• Proficiency in Python and frameworks such as TensorFlow, PyTorch, Flask, Langchain, Llamaindex, CrewAI and Autogen.
• Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models.
• Familiarity with computer vision techniques for image recognition, object detection, or image generation.
• Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment.
• Expertise in data engineering, including data curation, cleaning, and preprocessing.
• Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems.

Responsibilities

• Contribute to the design and implementation of AI solutions.
• Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI.
• Collaborate with stakeholders to identify business opportunities and define AI project goals.
• Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges.
• Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases.
• Integrate with relevant APIs and libraries, such as Azure OpenAI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities.
• Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment.
• Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs.
• Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs.
• Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly.
• Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency.
• Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of gen AI outputs for enterprise industry use cases.

Skills

Python

TensorFlow

PyTorch

Langchain

LlamaIndex

CrewAI

Autogen

Flask

NLP

Education

Bachelor's degree in CS/Engineering/Data Science or related

Tools

Azure

Azure OpenAI

HuggingFace

Redis

NoSQL

AWS

GCP

Job description

AI Engineer

This role is focused on designing and implementing AI solutions, leveraging techniques such as Language Models (LLMs) and generative AI to solve enterprise challenges.

Key responsibilities

• Contribute to the design and implementation of AI solutions.
• Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI.
• Collaborate with stakeholders to identify business opportunities and define AI project goals.
• Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges.
• Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases.
• Integrate with relevant APIs and libraries, such as Azure OpenAI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities.
• Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment.
• Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs.
• Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs.
• Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly.
• Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency.
• Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of gen AI outputs for enterprise industry use cases.

Prerequisites

• Bachelor's Degree in Computer Science, Engineering, Data Science, or any other relevant subject.
• Minimum 2 years of experience in Data Science and Machine Learning.
• Experience deploying LLMs, AI Agents and AI Solutions, and integrating and deploying open source AI models and solutions from Huggingface repository.
• In-depth knowledge of machine learning, deep learning, and generative AI techniques.
• Proficiency in programming languages such as Python, R, and frameworks like TensorFlow, PyTorch, Flask, Langchain, Llamaindex, CrewAI and Autogen.
• Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models.
• Familiarity with computer vision techniques for image recognition, object detection, or image generation.
• Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment.
• Expertise in data engineering, including data curation, cleaning, and preprocessing.
• Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems.