Senior Machine Learning Engineer (UAE Job Abu Dhabi UAE/Dubai,IT/Tech

Position: Senior Machine Learning Engineer (UAE)

CloudPSO is a Information Technology Outsourcing (ITO) company that assists in the acquisition of qualified staff to address complex digital problems in order to increase efficiency, reduce costs, and maintain compliance.

CloudPSO was founded in 2017 with an aim to provide businesses with a competent and skilled workforce at any given point in time and from any geographic region.

We are a US-based company with headquarters in Dallas (Texas) and a center of excellence in Pakistan. We have over 200 facility seats with an additional Work-From-Home facility. CloudPSO has skillful in-house software development teams with state-of-the-art tools, the latest VOIP technology platform, and secure infrastructure.

Our core values consist of client satisfaction, commitment, quality, and transparency.

We, at CloudPSO, hunt, analyze, recruit, train, and retain top-notch talent for you to help achieve your business goals. Optimizing mission-critical and day-to-day enterprise IT operations, CloudPSO enables businesses to transform, innovate and scale.

Job Description

This is a remote position.

Location: Remote – UAE
Requirement: A Valid UAE work permit/employment visa is mandatory.

Key Responsibilities

Regulation Parsing: Design and fine-tune Large Language Model (LLM) pipelines to interpret complex regulatory texts (e.g., military standards, building codes) and extract structured rules.
Rule Formalization: Convert natural language requirements into computer-processable formats (e.g., logic tuples) that can be executed by downstream compliance engines.
Semantic Search: Implement RAG (Retrieval-Augmented Generation) architectures to enable semantic querying of technical documentation and historical project data.
Prompt Engineering: optimize prompt strategies (few-shot learning, chain-of-thought) to improve model performance on domain-specific tasks without extensive retraining.

2. Predictive & Analytical Models (Supply Chain)

Forecasting Engines: Develop time-series forecasting models to predict material demand and spend categories, integrating internal ERP data with external market signals.
Risk Scoring: Build classification and anomaly detection models to assess supplier risk profiles based on financial health, delivery performance, and geopolitical factors.
Optimization Algorithms: Design algorithms for multi-objective optimization (e.g., balancing cost vs. lead time vs. risk) to support procurement decision-making.
Model Deployment: Containerize models using Docker/Kubernetes and deploy them into secure, on-premise inference environments.
Pipeline Orchestration: Build automated training and inference pipelines using tools like Kubeflow or MLflow to ensure reproducibility and scalability.
Performance Optimization: Optimize model inference latency and resource usage (e.g., quantization, distillation) to run efficiently on available hardware.
Monitoring & retraining: Implement monitoring systems to track model drift and performance in production, establishing feedback loops for continuous improvement.

Requirements

Core ML/AI: Expert proficiency in Python and standard ML libraries (PyTorch, Tensor Flow, Scikit-learn, Pandas, Num Py).
NLP & GenAI: Strong experience with transformer architectures (BERT, GPT, Llama) and NLP frameworks (Hugging Face, Lang Chain).
MLOps: Proficiency with MLOps tools and practices, including containerization (Docker), orchestration (Kubernetes), and experiment tracking (MLflow).
Data Handling: Ability to design data preprocessing pipelines for both structured (SQL, tabular) and unstructured (text, PDF) data.
Algorithm Design: Strong grasp of algorithmic principles for implementing custom logic, such as graph traversal or geometric computations.

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