Machine Learning Specialist

About This Role

***HIRING***

Job Title - Machine Learning Specialist

Salary - AED 26,800 Max. Per Month

Job Location – Dubai

Contract Length – 3 Months with extensions or to go perm

Start date – ASAP

I am working with a client who are a leading product consultancy in Dubai. They are looking for a ML Specialist to come in for an internal project, not outsourced to a customer. They have said they want to move to the next phase which is into AI but have not got a few key hires to make this happen, therefore, they need you to come in and work closely with the senior stakeholders to work out what is needed, and how they will do it. Right now, they still don’t know the platform they want to use, so they need a solid ML specialist to come onboard and support.

They are hiring an ML Specialist to work hand-in-hand with a Data Scientist to bring the AI-native SME banking platform to life. Where the Data Scientist focuses on research, modelling strategy, and insight generation, you will be the execution layer, responsible for engineering, optimising, and deploying the machine learning systems that power the product.

This is a highly technical, hands-on role for someone who thrives at the intersection of ML research and production engineering. You will ensure that what gets designed in notebooks actually works reliably at scale in a live financial product.

The client is looking for someone who can come in and work on reports straight away and be able to translate into tangible outcomes. They require someone who can connect with the team, and understand what they are doing, they do not want someone to be narrow minded, black and white, they want someone who can stretch the truth and see things others can’t.

Ideally, the client needs someone who has experience in finance sector, however someone without finance/banking can be trained. The non negotiables are someone who can walk the walk and talk the talk. They need someone who is a good talker, and confident. Overall, you just need to be innovative and has good ideas.

The client has said what they will reject is candidates who have polished CVs, but when they get on the call struggle with communication skills, therefore communication is key for this role. Another is when CVs do not match with job responsibilities, this will be rejected straight away. The client’s idea of a perfect candidate, is someone who can own the call, owns the meeting, and does not need babysitting, and is an individual leader. You will not have a project manager, therefore, they are looking for someone to manage their own workflows, and someone who is proactive.

Key Responsibilities

Model Engineering & Deployment

• Translate research models and prototypes from the Data Scientist into robust, production-grade ML systems
• Build and maintain scalable model training, evaluation, and inference pipelines
• Optimise models for latency, throughput, and cost without sacrificing accuracy
• Implement A/B testing and shadow-deployment frameworks for safe model rollout

MLOps & Infrastructure

• Own the MLOps stack model versioning, experiment tracking, CI/CD for ML, and automated retraining pipelines
• Set up and manage model monitoring, alerting, and drift detection in production
• Work with the engineering team to integrate ML services into the broader product architecture via clean APIs
• Contribute to infrastructure decisions around compute, GPU resources, and cloud ML services

LLM & Generative AI Integration

• Implement LLM-powered features such as intelligent financial summaries, SME advisory chatbots, and document understanding
• Apply RAG architectures, fine-tuning, and prompt engineering to ground generative outputs in real financial data
• Evaluate and benchmark foundation models to select the right tool for each use case

Collaboration & Quality

• Work closely with the Data Scientist to close the loop between experimentation and production
• Collaborate with product and engineering to define integration contracts and service boundaries for ML components
• Maintain high engineering standards code reviews, documentation, unit testing for ML code
• Ensure all ML systems meet the reliability and auditability requirements of a regulated financial product

Required Qualifications & Experience

• 3+ years of experience in machine learning engineering, applied AI, or a closely related role
• Strong Python skills with deep familiarity with ML frameworks (PyTorch, TensorFlow)
• Hands-on experience building and operating ML pipelines in production (not just notebooks)
• Solid MLOps experience, experiment tracking (MLflow, W&B), model registries, automated retraining, and monitoring
• Experience deploying and scaling ML services on cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML, or equivalent)
• Familiarity with LLMs and practical experience implementing RAG pipelines or fine-tuning workflows
• Strong software engineering fundamentals clean code, testing, version control, and CI/CD
• Able to work in a fast-moving product environment with evolving requirements

Nice to Have

• Experience with financial services data transaction streams, credit data, open banking APIs
• Knowledge of model explainability tooling (SHAP, LIME, Captum) relevant to regulated environments
• Familiarity with vector databases (Pinecone, Weaviate, pgvector) for semantic search and RAG
• Experience with real-time ML inference and event-driven architectures (Kafka, Flink, etc.)
• Understanding of UAE or GCC data privacy and AI governance requirements
• Prior experience in a startup or early-stage product environment

Thanks,

Ollie