Senior AI Engineer

Job Description:What will you do:1. Experimentation & EvaluationUnderstand 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 & PipelinesProfile 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 TechniquesWork 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 CoverageDuring 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. CollaborationCollaborate 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 ForWe 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 Engineer4–5 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 Engineer6+ 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.QualificationsThe 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 QualificationsGenerative 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: PythonLLM Runtime: AWS Bedrock, Google Vertex AI, Azure AI Foundry; DeepSeek/Qwen/GLM (China stack)Agentic Frameworks: AWS Strands, LangGraphData & Eval: Model SDKs, vector DBs, pandas/Jupyter-style tooling for experimentation