Artificial Intelligence Engineer

What we do:

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, productionize, 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.

- 12 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, audit logging, injection mitigation.

- Production: CI/CD, containers, logging/monitoring, performance and cost optimization.

Technical(nice to have):

- Experience with LangChain/LlamaIndex (or similar orchestration frameworks).

- Experience with vector databases and hybrid search implementations at scale.

- Cloud experience (AWS preferred) and data platforms (Databricks/Spark is a plus).

- Experience with observability tooling and LLM cost management/FinOps.

- Experience with responsible AI governance, red-teaming, or formal safety reviews.

Qualification & Experience:

- Graduation or Masters in Statistics, Mathematics, Computer Science or equivalent

- 4 years of software engineering / data engineering / applied AI experience with proven production delivery.

- 12 years building GenAI/LLM applications beyond demos (prototype production).