Applied AI Engineer (Agentic AI & ML)
$6,000–$8,500 year
On-siteSingapore, Singapore or Central, Louisiana, United States
Job Summary
Embed with business and operations stakeholders to identify high-value AI use cases and decompose ambiguous problems into deliverable solutions. Design and build production-grade agentic AI systems using LLMs, prompt engineering, RAG, and tool/function calling, while architecting multi-agent workflows and custom integrations into enterprise services. Own, maintain, and improve production ML/DL models by retraining, evaluating, and tuning them as data evolves. Deploy and operate applications on Microsoft Azure or GCP, implementing CI/CD pipelines, guardrails, and observability for non-deterministic AI systems. Turn bespoke builds into reusable internal patterns and route field learnings back into platform decisions.
Required Qualifications
- Machine Learning / Deep Learning (mandatory)
- Demonstrated hands-on experience building, training, evaluating, and deploying ML/DL models in production
- Solid ML fundamentals: evaluation, training, problem decomposition
- Applied & Agentic AI (mandatory)
- Hands-on experience with LLMs and prompt engineering
- Experience building agentic AI workflows and agent orchestration
- Working knowledge of MCP, RAG, vector databases, and LLM orchestration frameworks
- Understanding of production AI challenges: evals, guardrails, hallucination/quality control, model drift, observability
- NodeJS
- Python
- MCP
- REST API design and integration
- Microsoft Azure proficiency (mandatory) — App Services, Azure OpenAI, Functions, Storage, etc.
- Azure DevOps CI/C
- Docker
Desired Qualifications
- Experience with forecasting, predictive maintenance, or time-series modelling is strongly preferred
- Azure DevOps CI/C
- Docker (AKS is a plus)
- Google Cloud Platform (GCP)
- Full-stack development experience (frontend + backend)
- Frontend skills (React, Flutter)
- Python or Node.js for AI/ML orchestration
- Experience integrating AI into enterprise/industrial or operational technology systems
- Exposure to AI-assisted development tools and workflows
- Background in energy, utilities, or asset-heavy industries
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