AI Engineer

Elitez India; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Elitez India is seeking an experienced AI/Gen AI engineer to drive experimentation, model testing, and evaluation across POC workstreams in Singapore. You will design prompts, test retrieval strategies, and optimize outputs for alignment with business requirements, collaborating with PMs, developers, and SMEs.

You should be proficient in Python, Gen AI tooling, and cloud AI services; familiarity with China-origin models and agentic frameworks is a plus.

Qualifications

• 4+ years hands-on AI/ML or Gen AI engineering experience.
• Understanding of Gen AI concepts (tokenization, embeddings, RAG, prompting, evaluation).
• Familiar with at least one major cloud AI service (AWS Bedrock, Vertex AI, or Azure).
• Working knowledge of China AI models (DeepSeek, Qwen, GLM) is a strong plus.

Responsibilities

• 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 failure modes.
• Support SMEs in defining ground truth benchmarks for evaluation.
• Profile and clean sample datasets for experimentation (lightweight data prep).
• Build and test simple pipelines for data ingestion, prompt construction, and output evaluation.
• Work with foundation models via AWS Bedrock, Google Vertex AI, or Azure AI Foundry.
• Apply knowledge of China-origin models (DeepSeek, Qwen, GLM) as relevant.
• Apply agentic orchestration frameworks (AWS Strands, LangGraph) for multi-step workflows.
• Apply prompt strategies, RAG design, and vectorisation for KBs.
• Provide insights and recommendations to improve model performance; discuss fine-tuning where applicable.

Skills

Experimentation design

Gen AI engineering

Python

Prompt engineering

RAG & embeddings

Cloud AI services

Data wrangling

Multi-step agent workflows

Tools

AWS Bedrock

LangGraph

Strands

Vector databases

Job description

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

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.

• 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).
• 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
• Agentic Frameworks: AWS Strands, LangGraph