Responsibilities
AI Solution Development & Software Engineering
- Design, develop, and maintain production-grade APIs and AI-enabled applications
- Integrate AI workflows with enterprise platforms and third-party systems
- Develop scalable and reliable software architectures with logging, tracing, and monitoring capabilities
- Ensure software quality through clean coding practices, testing, performance tuning, and system optimization
AI/ML Engineering & MLOps
- Build and operationalize end-to-end AI/ML solutions, including model training, deployment, monitoring, and continuous improvement
- Develop and maintain CI/CD pipelines for AI/ML systems to support automation, reproducibility, and version control
- Implement monitoring, telemetry, observability, and model drift detection practices
- Support incident investigation, troubleshooting, and system reliability improvements
Data Engineering & AI Workflows
- Develop and optimize data ingestion, preprocessing, and feature engineering pipelines for training and inference workloads
- Work with structured, unstructured, and multimodal datasets
- Implement embedding pipelines, vector database integrations, and Retrieval-Augmented Generation (RAG) workflows
- Support AI agent development and workflow automation initiatives
Model Development & Optimization
- Perform model evaluation, validation, hyperparameter tuning, and optimization activities
- Fine-tune AI/ML models to improve robustness, accuracy, and operational efficiency
- Support deployment and lifecycle management of AI/ML solutions in production environments
Requirements
- Bachelor's Degree in Computer Engineering, Computer Science, Data Science, Information Technology, or a related discipline
- Minimum 5 years of experience in AI Engineering or Machine Learning Engineering, or at least 8 years of experience in Software Engineering and DevOps
- Proficient in Python, JavaScript, API integration, and cloud platforms (Azure preferred)
- Experience developing AI agents, GPT-style applications, or workflow automation systems
- Strong understanding of data pipelines, embedding models, vector databases, prompt engineering, and model evaluation methodologies
- Proven experience delivering end-to-end AI/ML solutions from data ingestion and preprocessing to deployment and lifecycle management
- Hands-on experience with MLOps practices, including CI/CD pipelines, versioning, retraining, and performance monitoring
- Experience with machine learning frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers
- Familiarity with Docker, Kubernetes, GitLab CI, Jenkins, Terraform, and monitoring/logging platforms
- Exposure to Industry 4.0, Digital Twin, industrial AI/ML solutions, or optimization use cases will be an added advantage
- Ability to work independently and collaboratively in a fast-paced environment
We regret to inform that only shortlisted candidates will be notified.
By responding to Yokogawa's advertisement, consent is considered given to Yokogawa to collect the required personal data for the purpose of recruitment with expectation that Yokogawa will protect personal data with security safeguards that are reasonable and appropriate to the sensitivity of the personal data, to protect it from unauthorized access, use or disclosure and complies with applicable regulatory requirements with respect to the retention of personal data.
Yokogawa is an Equal Opportunity Employer. Yokogawa wants a diverse, equitable and inclusive culture. We will actively recruit, develop, and promote people from a variety of backgrounds who differ in terms of experience, knowledge, thinking styles, perspective, cultural background, and socioeconomic status. We will not discriminate based on race, skin color, age, sex, gender identity and expression, sexual orientation, religion, belief, political opinion, nationality, ethnicity, place of origin, disability, family relations or any other circumstances. Yokogawa values differences and enables everyone to belong, contribute, succeed, and demonstrate their full potential.
