Job Description

AI Engineer (Data Science Tech)About the Role

We are looking for 2 experienced AI Engineers to join client's Data Science Tech team. While our core Data Science team focuses on building and training models, this role is entirely focused on productionizing, integrating, and managing those models.

You will be the critical bridge between data science and our broader IT ecosystem. Your primary mission is to take Generative AI and machine learning models out of the lab and turn them into scalable, end-to-end applications (including Agentic solutions) that deliver real business value.

Because the MLOps and Model Management practices are still in their early stages, they are looking for a flexible, problem-solving engineer to help us define and build this infrastructure from the ground up.

Key Responsibilities

End-to-End Application Development: Design and develop robust backend systems and intuitive user interfaces (AI UIs) to serve Generative AI and Agentic solutions to end-users.
System Integration: Seamlessly integrate AI models and GenAI capabilities into our existing IT ecosystem and enterprise applications.
MLOps & CI/CD: Build, deploy, and maintain flexible MLOps and AIOps pipelines. Implement CI/CD best practices specifically tailored for machine learning workflows.
Model Management (Production): Oversee the lifecycle of models in production—including deployment, versioning, scaling, and performance monitoring. (Note: You will help us define what good looks like here as our infrastructure evolves).
Cross-Functional Collaboration: Partner closely with the core Data Science team (who handles primary model development) to ensure models are deployment-ready, and work collaboratively with our Fractional Lead in Singapore to align on technical strategy.

Requirements & Qualifications: At least 7 years of experience

Production AI Experience (Must-Have): You have a proven track record of deploying, running, and managing AI/ML models in a live production environment. (Note: Simply using AI coding assistants like Claude Codex or wrapping public APIs for personal projects is not sufficient for this role).
Backend & Application Engineering: Strong software engineering background (Python, Node.js, Go, or similar) with experience building scalable backend architectures and APIs.
GenAI/LLM Integration: Hands-on experience integrating Generative AI models into software applications (e.g., using frameworks like LangChain, LlamaIndex, or building RAG architectures).
MLOps & Infrastructure Flexibility: Experience with model deployment and management tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI, Docker, Kubernetes). You should be comfortable navigating ambiguity and recommending the right tools as our requirements crystallize.
UI/Frontend Familiarity: Experience building or integrating AI UIs (e.g., Streamlit, Gradio, React, or standard web frameworks) to demonstrate and serve AI solutions.
Strong Communicator: Ability to work effectively in a distributed team (Vietnam & Singapore) and articulate technical trade-offs to stakeholders who may still be defining their requirements.

Other Info