Lead Consultant

ELLIOTT MOSS CONSULTING PTE. LTD.; Singapore; On-site; SGD 100,000 - 180,000; Full time

Job summary

Elliott Moss Consulting PTE. LTD. in Singapore seeks an AI Engineer to design, develop and deploy scalable AI/ML solutions using Google Cloud and Generative AI.

You will work closely with data scientists, ML Engineers, UX Designers, Product Managers and Cloud Architects to deliver enterprise-grade AI experiences. The role blends hands-on engineering with AI/UX, focusing on production-ready models, data pipelines, MLOps and governance.

Qualifications

• Bachelor's degree in CS, IT or related field.
• 3+ years of AI/ML engineering experience.
• Hands-on experience with Python and ML frameworks.
• Experience with Generative AI, LLMs and embeddings.
• Hands-on Google Cloud AI / Vertex AI and BigQuery.
• Familiarity with MLOps, data pipelines and model deployment.

Responsibilities

• AI/ML Engineering & Solution Development: Design, develop and support scalable AI/ML solutions for enterprise use cases.
• Develop and implement AI/ML algorithms and solutions based on business and user requirements.
• Build data processing workflows for extracting, transforming and loading large volumes of structured and unstructured data.
• Prepare, integrate and transform data required for AI/ML model development and deployment.
• Work with Data Scientists and ML Engineers to convert analytical and theoretical models into production-ready AI solutions.
• Conduct experiments to evaluate model performance, accuracy, scalability and reliability.
• Identify, troubleshoot and resolve issues affecting deployed AI/ML solutions.
• Apply statistics, scripting and programming skills to develop and optimise AI solutions.

Skills

Python

ML Engineering

Generative AI

LLMs

Vertex AI

BigQuery

MLOps

Agile

Data Pipelines

Cloud Platforms

Education

Bachelor's in CS/IT/AI related field

Tools

BigQuery

Vertex AI

Google Cloud

Python

Job description

Job Description

We are looking for an AI Engineer with strong expertise in Artificial Intelligence, Machine Learning, Generative AI, Google Cloud and AI-powered User Experience (UX) to design, develop and deliver scalable enterprise AI solutions.

The role combines hands-on AI/ML engineering with AI/UX design, working closely with Data Scientists, ML Engineers, UX Designers, Product Managers, Cloud Architects and business stakeholders to build intelligent, user-centric and enterprise-ready solutions.

Key Responsibilities

• AI/ML Engineering & Solution Development Design, develop and support scalable and optimised AI/ML solutions for enterprise use cases.
• Develop and implement AI/ML algorithms and solutions based on business and user requirements.
• Build data processing workflows for extracting, transforming and loading large volumes of structured and unstructured data.
• Prepare, integrate and transform data required for AI/ML model development and deployment.
• Work with Data Scientists and ML Engineers to convert analytical and theoretical models into production-ready AI solutions.
• Conduct experiments to evaluate model performance, accuracy, scalability and reliability.
• Identify, troubleshoot and resolve issues affecting deployed AI/ML solutions.
• Apply statistics, scripting and programming skills to develop and optimise AI solutions.
• Work with relevant software platforms and cloud environments where AI/ML models are developed and deployed.
• Google Cloud AI & Generative AI Design and deliver AI solutions using Google Cloud AI services, including Vertex AI and managed ML services.
• Develop enterprise use cases using Generative AI, LLMs, foundation models, embeddings and conversational AI.
• Design AI/ML pipelines covering data ingestion, processing, model development, testing, deployment and monitoring.
• Work with BigQuery, data pipelines and Google Cloud analytics services to support AI/ML workflows.
• Integrate Google AI APIs for use cases including Natural Language, Vision, Speech, Search and Recommendations.
• Evaluate and experiment with emerging Google AI capabilities and translate them into practical enterprise solutions.
• Balance custom AI/ML development with managed Google Cloud services based on performance, scalability, cost and business requirements.
• MLOps & Model Lifecycle Support model training, validation, deployment, monitoring and lifecycle management.
• Implement appropriate MLOps practices for model versioning, deployment and monitoring.
• Monitor deployed models and identify opportunities for performance optimisation.
• Support continuous improvement of AI/ML solutions based on model performance and user feedback.
• Ensure AI solutions are scalable, reliable and suitable for enterprise production environments.
• AI/UX & User Experience Design AI-powered experiences that translate complex AI capabilities and model outputs into intuitive user interactions.
• Work with UX teams to integrate AI capabilities across web, mobile, conversational interfaces, dashboards and enterprise applications.
• Design interfaces that allow users to understand, validate and act on AI-generated insights.
• Develop explainable and transparent AI experiences that improve user trust and adoption.
• Design human-in-the-loop workflows where users can review, challenge and provide feedback on AI outputs.
• Contribute to user research, journey mapping, prototyping and usability testing for AI-powered products. Translate user needs and business requirements into practical AI solution designs.
• Data, Analytics & AI Insights Work with Data Scientists, Data Engineers and Analytics teams to prepare and utilise data for AI solutions.
• Analyse complex datasets and AI outputs to identify actionable business insights.
• Define and track AI solution KPIs including model performance, adoption, accuracy, user engagement and business impact.
• Support experimentation and A/B testing to evaluate AI features and user experiences.
• Assess data quality, bias and representativeness to support reliable AI outcomes.
• Responsible AI & Governance Apply responsible AI principles throughout the design and development lifecycle.
• Identify and address risks relating to bias, fairness, privacy, security, transparency and explainability.
• Support responsible use of enterprise data in AI/ML solutions.
• Ensure AI solutions align with applicable data protection, security and regulatory requirements.
• Promote transparent and human-centred AI design practices.
• Stakeholder & Client Collaboration Collaborate with Product Managers, UX Designers, Data Scientists, Data Engineers, Cloud Architects and business stakeholders.
• Translate business requirements into technical AI/ML solutions and user-centric experiences.
• Lead technical discussions, AI solution workshops and design-thinking sessions with stakeholders.
• Communicate AI concepts, model outputs and technical considerations to both technical and non-technical audiences.
• Support client engagements, solution proposals, proof-of-concepts and AI transformation initiatives.

Qualifications & Experience

• Bachelor's in Computer Science, Information Technology, AI/ML or related fields.
• 3+ years of professional experience in AI/ML engineering, AI solution development or related AI engineering roles.
• Strong hands-on experience developing and deploying AI/ML solutions.
• Experience with data extraction, transformation, integration and processing for AI/ML applications.
• Strong programming/scripting skills in Python or equivalent programming languages.
• Strong understanding of machine learning, statistics, algorithms and model evaluation.
• Experience working with Generative AI, LLMs, embeddings and conversational AI.
• Hands-on experience with Google Cloud AI / Vertex AI or comparable enterprise AI/ML platforms.
• Experience with BigQuery, cloud data pipelines and analytics platforms.
• Understanding of MLOps, including model training, deployment, monitoring, versioning and lifecycle management.
• Experience troubleshooting and optimising deployed AI/ML models.
• Experience designing or contributing to AI-powered UX and enterprise digital experiences.
• Knowledge of UX principles, user research, prototyping and human-centred design is highly desirable.
• Experience with Figma or similar UX/prototyping tools is an advantage.
• Strong understanding of responsible AI, explainability, data privacy and AI governance.
• Excellent communication, stakeholder management and presentation skills.
• Experience working in Agile, Design Thinking or cross-functional product development environments.