AI/ML Engineer

Job Location - Dubai

Job Summary

· Lead the design, development, and deployment of AI/ML solutions to enhance operational efficiency, reduce fraud, improve risk assessment, and enable personalized digital services in insurance and financial services.

· Serve as the AI/ML subject matter expert, ensuring compliance with ethical AI principles, regulatory mandates, and data privacy standards.

· Leverage modern AI/ML platforms (Crew AI, n8n, Autogen, LangGraph, LangChain, LangSmith) and workflow automation tools (n8n) to accelerate solution development, integration, and deployment.

· Drive the transition of AI/ML solutions from POC to production-scale environments with techniques like reranking, prompt engineering, and LLM orchestration.

· Define and enforce AI/ML guardrails, ensuring safety, fairness, explainability, and compliance across all deployed models.

Main Functions & Responsibilities

1. Machine Learning & Deep Learning

· Design, train, and deploy supervised, unsupervised, and reinforcement learning models.

· Expertise in predictive modeling for classification, regression, and time-series forecasting.

· Knowledge of deep learning architectures: CNNs, RNNs, LSTMs, Transformers, and attention mechanisms.

1. Natural Language Processing (NLP)

· Text processing, tokenization, and embedding generation.

· Information extraction, summarization, question answering, and sentiment analysis.

· Working with large language models (LLMs) and knowledge graphs.

1. Computer Vision

· Image classification, object detection, segmentation, and OCR.

· Processing scanned documents, medical imaging, and other unstructured visual data.

1. Model Deployment & MLOps

· Productionizing models with automated pipelines: training, testing, deployment, monitoring, and retraining.

· Performance optimization, scaling, and reliability in production environments.

· Monitoring and logging model outputs for accuracy and drift detection.

1. Prompting, Reranking & LLM Orchestration

· Designing and optimizing prompts for LLMs and generative AI systems.

· Reranking outputs to improve relevance and accuracy of AI responses.

· Orchestrating multiple models or LLMs to solve complex workflows.

1. AI Governance & Ethics

· Embedding explainability, interpretability, and transparency into models.

· Bias detection, mitigation, and fairness in AI outputs.

· Implementing AI/ML guardrails to prevent unsafe or non-compliant behaviors.

1. Data Engineering & Feature Engineering

· Data preprocessing, cleaning, and transformation for structured and unstructured data.

· Feature selection, feature creation, and dimensionality reduction.

· Integration of heterogeneous data sources (databases, APIs, documents, streaming data).

1. Analytics & Decision Support

· Building models to support business decisions (risk scoring, fraud detection, customer segmentation).

· Behavioral analytics, recommendation engines, and personalization strategies.

· Evaluating and validating model performance against business KPIs.

1. Cloud & Scalable AI Infrastructure

· Working with cloud platforms for AI model training and deployment.

· Using distributed computing, GPU/TPU acceleration, and serverless architectures.

· Ensuring compliance with data residency and privacy requirements.

1. Collaboration & Communication

· Translating business problems into AI solutions.

· Communicating model results and insights to stakeholders.

· Mentoring junior engineers and knowledge sharing across teams.

Academic Background

· Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or Applied Mathematics.

· Strong foundation in probability, statistics, econometrics, optimization, and financial mathematics.

Qualifications & Experience

Experience

· 4–5 years of AI/ML engineering experience and working on the AI platforms as mentioned above, preferably in insurance, healthcare, or financial services.

· Proven ability to transition POC models to production at scale with measurable business impact (fraud reduction, claims automation, risk scoring, personalization).

Technical Skills

· Programming: Python (preferred), R, Java.

· ML/DL Frameworks: TensorFlow, PyTorch, Hugging Face.

· AI/LLM Platforms: Crew AI, LangGraph, LangChain, LangSmith,Auto Gen

· Workflow Automation: n8n.

· Techniques: Reranking, prompt engineering, LLM orchestration, AI/ML guardrails.

· Specializations: NLP (claims text, policy documents, medical records), Computer Vision (scanned documents, medical imaging).

· Cloud Platforms: AWS SageMaker, Azure AI, GCP Vertex AI (data residency compliant).

· MLOps Tools: MLflow, Kubeflow, Docker, Kubernetes, Airflow.

· Domain Knowledge: Insurance systems (CMS, TPAs, broker portals) and financial data models.

Soft Skills

· Translate business challenges into AI/ML solutions with production-scale impact.

· Strong stakeholder communication, executive reporting and mentoring skills.

· Expertise in embedding AI/ML guardrails and ethical frameworks in deployed solutions.