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Open nowPosted 6 days ago

AI Engineer

MyCareersFuture94,028 open roles

Pay
SGD 5,000 – SGD 11,000 a month
Where
Singapore
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Your applicationOpen nowAI EngineerMyCareersFuture · Singapore

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This job: posted 6 days ago

The posting

Job Description: What will you do:

1. Experimentation & Evaluation

  • Understand the business problem, POC objectives, and evaluation metrics.
  • Design experiments to test different model configurations, prompts, or retrieval strategies.
  • Analyse Gen AI outputs for quality, accuracy, and alignment with requirements; identify common failure modes (hallucination, bias, irrelevant answers, factual errors).
  • Support SMEs in defining ground truth benchmarks for evaluation.

2. Data Preparation & Pipelines

  • Profile and clean sample datasets for experimentation (lightweight data prep).
  • Build and test simple pipelines for data ingestion, prompt construction, and output evaluation.

3. Gen AI & Agentic Techniques

  • Work with foundation models via AWS Bedrock, Google Vertex AI, or Azure AI Foundry depending on engagement cloud posture.
  • Apply working knowledge of China-origin models (DeepSeek, Qwen, GLM) as increasingly relevant, cost-effective alternatives.
  • Apply agentic orchestration frameworks such as AWS Strands, LangGraph, or equivalent, for designing and testing multi-step agent workflows.
  • Apply prompt strategies, prompt engineering patterns, and RAG design (chunking, embeddings, retrieval evaluation); support ingesting/vectorising content to knowledge bases.
  • Provide insights and recommendations to improve model performance in quick iterations, including fine-tuning approaches where applicable.

4. FDE & Development/Maintenance Coverage

  • During FDE engagements: rapidly test candidate models, prompts, and retrieval strategies, giving the team fast, evidence-based go/no-go signals.
  • During system development & maintenance engagements: support ongoing model/prompt tuning and monitoring as applications move toward production.

5. Collaboration

  • Collaborate with developers on integrating models into the POC workflow, and work closely with PM, devs, and SMEs to refine data and prompts.
  • Partner with the Data Scientist on evaluation methodology where classical statistical baselines are in play, and with the AI/LLM Specialist when an engagement moves toward production-grade evaluation.
  • Document experiments briefly but clearly (hypothesis → result → conclusion).

Role Levels We Are Hiring For

We are hiring at two levels for this role. Allresponsibilities above apply to both; the distinction is in scope of ownership,years of experience, and seniority of judgement expected.

AI Engineer

  • 4–5 years of hands-on experience in AI/ML or Gen AI engineering. Runs experiments and prototypes independently within a defined POC/POV scope, under guidance from a Senior AI Engineer or AI/LLM Specialist.
  • Executes rapid experimentation cycles for one engagement at a time; escalates ambiguous evaluation calls to senior team members.

Senior AI Engineer

  • 6+ years of hands-on experience, including prior ownership of experimentation strategy for complex or ambiguous problem statements. Sets the experimentation approach across multiple engagements and mentors junior AI Engineers.
  • Advises PMs and stakeholders directly on feasibility and experimentation trade-offs; represents technical experimentation findings in client conversations.

Qualifications

The ideal candidate should possess:

  • 4+ years hands-on experience in AI/ML or Gen AI engineering (see Role Levels for the split between AI Engineer and Senior AI Engineer).
  • Understanding of Gen AI concepts (tokenization, embeddings, RAG, prompting, evaluation).
  • Familiar with at least one major cloud AI service (AWS Bedrock, Google Vertex AI, or Azure AI Foundry); working knowledge of others a plus.
  • Working knowledge of the China AI model landscape (DeepSeek, Qwen, GLM) a strong plus.
  • Familiarity with agentic orchestration frameworks (AWS Strands, LangGraph, or equivalent), for designing and testing multi-step agent workflows.
  • Ability to do rapid experimentation rather than perfect models.
  • Basic proficiency in Python and Gen AI tools (e.g., model SDKs, vector DBs).
  • Analytical mindset: can quantify subjective output (accuracy, relevance, readability).
  • Good data wrangling skills to prepare small datasets quickly.

Preferred Qualifications

  • Generative AI Leader or Machine Learning Engineer certification, or equivalent.
  • Exposure to LLMOps practices (model monitoring, versioning) for production transition.
  • Familiarity with model fine-tuning techniques.
  • Exposure to regulated government cloud environments.

Tech Stack (Illustrative)

  • Languages: Python
  • LLM Runtime: AWS Bedrock, Google Vertex AI, Azure AI Foundry; DeepSeek/Qwen/GLM (China stack)
  • Agentic Frameworks: AWS Strands, LangGraph
  • Data & Eval: Model SDKs, vector DBs, pandas/Jupyter-style tooling for experimentation
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