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Job Description & How to Apply Below
GMG is a global well-being company retailing, distributing and manufacturing a portfolio of leading international and home-grown brands across sport, everyday goods, health and beauty, properties and logistics sectors. Under the ownership and management of the Baker family for over 45 years, GMG is a valued partner of choice for the world's most successful and respected brands in the well-being sector.
Working across the Middle East, North Africa, and Asia, GMG has introduced more than 120 brands across 12 countries. These include notable home-grown brands such as Sun & Sand Sports, Dropkick, Supercare Pharmacy, Farm Fresh, Klassic, and international brands like Nike, Columbia, Converse, Timberland, Vans, Mama Sita's, and McCain.
What will you do:
We are looking for an AI Engineer (GenAI & Agents) to build, product ionize, and operate GenAI solutions that improve business workflows and customer experiences across a large omni-channel retail environment. You will work hands-on across LLM application engineering (RAG, tool/function calling, agent workflows), quality evaluation, safety/guardrails, and reliable deployment at scale.
In Brief:
- Build GenAI applications (RAG + tools/agents) from prototype to production.
- Integrate LLM workflows with enterprise data, APIs, and internal systems.
- Implement evaluation, guardrails, privacy/security controls, and observability.
- Optimize for latency, cost, reliability, and continuous improvement in production.
Responsibilities:
Build & ship GenAI solutions:
- Design and implement LLM-backed applications using patterns like RAG, tool/function calling, workflows, and agent-like orchestration where appropriate.
- Develop APIs/services for GenAI capabilities (chat, copilots, summarization, classification, content generation, knowledge assistants).
- Build reusable components (prompt templates, tool registries, orchestration patterns, guardrail modules) to accelerate delivery.
Knowledge & retrieval engineering (RAG)
- Build ingestion pipelines for knowledge sources (documents, KB articles, policies, FAQs, runbooks) with metadata and refresh cadence.
- Implement retrieval strategies (vector + hybrid search, reranking, filtering, citations) and tune chunking/embedding approaches.
- Enforce permission-aware retrieval (RBAC/ABAC) and ensure answers are grounded with references.
Agents & integrations:
- Implement agent workflows that can call tools safely (search, ticketing actions, calculations, data lookups) with strict controls.
- Integrate with enterprise services via APIs (identity, data platforms, operational systems, knowledge repositories).
- Design human-in-the-loop patterns for sensitive workflows and escalation paths.
Quality, evaluation, and safety:
- Create evaluation harnesses and regression tests (groundedness, relevance, factuality, refusal behavior, latency, cost).
- Implement safety guardrails (PII handling, prompt injection defenses, policy constraints, output validation, moderation where needed).
- Establish feedback loops: capture user signals, label errors, and continuously improve retrieval and prompts.
Production engineering & LLMOps:
- Deploy and operate services with proper CI/CD, monitoring, and incident response.
- Implement caching, rate limits, fallbacks, and cost controls.
- Maintain documentation, runbooks, and operational KPIs/SLOs for GenAI services.
Technical
Competencies:
- 4 years of software engineering / data engineering / applied AI experience with proven production delivery.
- 1–2 years building GenAI/LLM applications beyond demos (prototype → production).
- Strong stakeholder collaboration and ability to work in ambiguous problem spaces.
- Comfortable owning systems end-to-end: build, deploy, monitor, and improve.
Technical(mandatory):
- Strong Python engineering (APIs, testing, async, packaging, clean code).
- RAG fundamentals: embeddings, chunking, retrieval tuning, reranking, grounding/citations.
- LLM integration: structured outputs, tool/function calling, context management, prompt design.
- Evaluation: test sets, automated evaluation + human review loops, regression testing.
- Security/privacy basics: PII handling, permissioned retrieval,…
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