Senior Software Consultant

APBA TG HUMAN RESOURCE PTE. LTD.; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

APBA TG HUMAN RESOURCE PTE. LTD. in Singapore seeks an AI Engineer or Senior AI Engineer to lead rapid experimentation and evaluation across POC engagements, designing experiments and collaborating with PMs, devs, and SMEs to refine data and prompts.

The role includes data preparation, building lightweight pipelines, applying agentic frameworks, and tuning models for production-readiness while aligning with client requirements.

Qualifications

• 4+ years hands-on experience in AI/ML or Gen AI engineering.
• 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 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.

Responsibilities

• Design experiments to test model configurations, prompts, or retrieval strategies.
• Analyse Gen AI outputs for quality, accuracy, and alignment with requirements; identify failure modes.
• Build and test simple data ingestion and evaluation pipelines for experiments.
• Support rapid testing during FDE engagements and production readiness.
• Collaborate with PMs, devs, and SMEs to refine data and prompts; document results.

Skills

AI/ML engineering

Gen AI concepts

Cloud AI services

China AI models

Agentic orchestration

Python

Data wrangling

Experiment design

Tools

Model SDKs

Vector DBs

Pandas/Jupyter

Job description

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. All responsibilities 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