AI Adoption And Transformation Lead

finera.; Dubai; On-site; AED 350,000 - 700,000; Full time

Benefits offered by this job

Medical Insurance starting day 1

Training resources

Well-stocked office

Team-building events

Employee Recognition Program

Job summary

Finera is seeking a senior, hands-on ML engineer who has shipped LLM systems to production and can run them reliably in a payments/regulatory context. You will own end-to-end LLM pipelines, ensure observability, and drive performance and cost efficiency.

You will design RAG architectures, implement evaluation loops, and collaborate with security, risk, and compliance to meet strict governance needs. A strong background in Python, cloud platforms (AWS/GCP), containers, and IaC is required.

Qualifications

• 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.

Responsibilities

• Ship production LLM applications.
• Own the evaluation loop.
• Engineer for reliability.
• Optimise cost and latency.
• Partner on governance.
• Set technical direction.

Skills

Python

LLM orchestration

RAG pipelines

Evaluation framework

ML fundamentals

Production cloud

Education

Bachelor's degree or higher in CS/ML

Tools

CI/CD

Containers

Infrastructure as Code

Job description

Job Description:

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

• 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.

Nice-to-Have / Preferred

• 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.

Job Responsibilities

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.

Own the evaluation loop

• Build offline and online eval harnesses, golden datasets, and regression suites so every prompt, model, and retrieval change is measured before it ships.

Engineer for reliability

• Add guardrails, output validation, prompt-injection defences, structured fallbacks, and observability so LLM behaviour is predictable and auditable.

Optimise cost and latency

• Manage context engineering, caching, model routing across providers, and streaming inference to hit strict performance and unit-economics targets.

Partner on governance

• Work with security, risk, and compliance on data handling, PII redaction, residency, and the auditability a regulated payments business requires.

Set technical direction

• Lead architecture reviews for GenAI, mentor engineers, and raise the bar for how the team builds with LLMs.

Job Benefits

• 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.