AI Adoption and Transformation Lead
Panamari Holding
Date: 3 weeks ago
City: Dubai
Contract type: Full time
Job Description
This is a senior, hands-on role for an engineer who has already shipped LLM systems to production and understands what it takes to run them reliably where mistakes have real financial and regulatory consequences.
Job Requirements
Job Requirements
Ship production LLM applications
This is a senior, hands-on role for an engineer who has already shipped LLM systems to production and understands what it takes to run them reliably where mistakes have real financial and regulatory consequences.
Job Requirements
Job Requirements
- 5+ years in software engineering, with 2+ years shipping LLM/GenAI systems to production.
- Strong Python and hands-on experience with LLM orchestration
- RAG in depth - embeddings, vector stores, retrieval quality, chunking, and how to evaluate all of it.
- Evaluation-first mindset - you build measurement into LLM systems rather than shipping on vibes.
- Solid ML grounding - transformer architectures, tokenisation, context windows, and their practical trade-offs.
- Production cloud - AWS or GCP, containers, CI/CD, and infrastructure-as-code for reliable, observable services.
- Regulated domain - fintech, payments, or another compliance-heavy environment.
- Fine-tuning - adapters/LoRA, distillation, and knowing when not to.
- Agentic systems - multi-step tool orchestration, MCP, and long-running workflows.
- Real-time inference - streaming, low-latency serving, and throughput tuning.
- Standards fluency - SOC 2, PCI DSS, GDPR, and data-residency considerations.
- Open-weight models- self-hosting and optimisation alongside hosted APIs.
Ship production LLM applications
- Design and build RAG pipelines, agentic and tool-calling workflows, and copilots that plug into the payments platform and our internal operations.
- Build offline and online eval harnesses, golden datasets, and regression suites so every prompt, model, and retrieval change is measured before it ships.
- Add guardrails, output validation, prompt-injection defences, structured fallbacks, and observability so LLM behaviour is predictable and auditable.
- Manage context engineering, caching, model routing across providers, and streaming inference to hit strict performance and unit-economics targets.
- Work with security, risk, and compliance on data handling, PII redaction, residency, and the auditability a regulated payments business requires.
- Lead architecture reviews for GenAI, mentor engineers, and raise the bar for how the team builds with LLMs.
- Competitive salary package aligned with experience and market standards.
- Medical Insurance starting from day 1.
- Access to training resources and development opportunities that support your professional growth.
- Well-stocked office with snacks, drinks, and refreshments available daily.
- A multinational organisation that promotes a strong, collaborative culture
- Regular team-building events and company activities that strengthen collaboration across teams.
- Employee Recognition Program celebrating our "Employee of the Month" with special perks.
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