AI Engineer

Company Description

Yondu, a leading IT solutions company owned by Globe Telecom and based in the Philippines, empowers businesses across industries with innovative technology solutions for success in the digital age. Established in 2001, Yondu offers growth opportunities within a dynamic and diverse culture powered by innovation and collaboration. The company values its employees by supporting their personal and professional growth via upskilling initiatives, certification programs, and training. With a flexible work-from-anywhere setup and a focus on recognizing excellence, Yondu provides an engaging environment for talented individuals to build rewarding IT careers.

Role Description

This is a full-time hybrid role for an AI Engineer based in Singapore, with flexibility for remote work. The AI Engineer will design, build, and optimize Artificial Intelligence and Machine Learning systems. Responsibilities include developing and implementing machine learning algorithms, conducting pattern recognition analysis, working with neural networks, and applying Natural Language Processing (NLP) techniques to solve complex challenges. Collaboration with cross-functional teams will be integral for delivering innovative and efficient software solutions.

Capability Requirements:

The AI Engineer requires hands-on expertise in developing and deploying Large Language Models, Retrieval Augmented Generation pipelines, and Machine Learning models that support automation, incident analysis, and operational efficiency. The AI Engineer must demonstrate the ability to design end-to-end AI workflows, configure vector databases, implement chunking strategies for retrieval, and build ML models that detect anomalies in logs and system events. The capability includes strong proficiency in Python, model lifecycle management, prompt engineering, responsible AI practices, and integrating AI components into production environments.

Duties and Responsibilities

• Build LLM modules for RCA, Incident Summaries, correlation logic, and remediation workflows using operational data sources such as SOPs, ELK logs, and ServiceNow tickets.
• Develop machine learning models to detect anomalies in system and infrastructure logs.
• Integrate AI solutions with existing platforms to enhance monitoring, reporting, and automation capabilities.
• Ensure that AI models follow responsible AI practices, governance requirements, and documentation standards.
• Collaborate with cross functional teams to refine AI requirements, support testing, and improve model outputs.

Job Specifications

• Relevant Work Experience: The role requires practical experience in developing and deploying AI systems, including:
• Hands-on experience designing transformer based workflows, dynamic prompts, and LLM driven automation for operational use cases such as incident analysis and RCA.
• Experience building tokenization pipelines, embeddings, prompt engineering, and fine tuning using frameworks such as Hugging Face, LangChain, and OpenAI libraries.
• Experience deploying and configuring vector databases for semantic search, including HNSW indexing, metadata filtering, and sharding using platforms such as Milvus, Pinecone, or Azure AI Search.
• Experience implementing Retrieval Augmented Generation pipelines with chunking strategies such as semantic chunking, sliding window, or hierarchical chunking.
• Experience integrating LLM workflows with enterprise systems such as ELK Stack, SOP repositories, and ServiceNow tickets.

• Experience with ML model lifecycle management including training, evaluation, deployment, and performance monitoring.

• Knowledge & Skills

• Primary Knowledge (Core for the Role)
• Large Language Models and Generative AI.
• Transformer architectures, embeddings, prompt engineering, and LLM tuning techniques.
• Retrieval pipelines and vector indexing techniques.
• RAG architectures and context optimization methods.
• Vector databases and similarity search including HNSW indexing, metadata filtering, and sharding.
• Embedding generation, chunking strategies, and retrieval workflows using LangChain, LlamaIndex, or similar tools.

Secondary

• Machine learning concepts including supervised, unsupervised, and deep learning methods.
• Feature engineering using Pandas, NumPy, Scikit-Learn, FeatureTools, PyCaret, or similar tools.
• Time series forecasting models such as ARIMA, LSTM, Prophet, VAE, and related techniques.
• Supervised learning models such as IsolationForest, XGBoost, LightGBM, and RandomForest.
• Model deployment using Docker, Kubernetes, MLflow, Airflow, Kubeflow, Azure ML, SageMaker, or GitHub Actions.
• Drift monitoring using EvidentlyAI, WhyLabs, Prometheus, Grafana, ELK Stack, LangSmith, or Weights and Biases.
• Automated pipelines for training, validation, deployment, and performance monitoring of ML models.