AI Engineer

Responsibilities:

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.

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.

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.

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.

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).

Requirements:

• 3 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 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

Interested candidates may send their CV to MAC (Reg No. R1221300) atanc1@anchorsearchgroup.com quoting the job title in the Subject line. We regret that only shortlisted candidates will be notified.