Senior AI/ML Engineer

Dicetek LLC; Dubai; On-site; AED 441,000 - 661,000; Full time

Job summary

Dicetek LLC is seeking a senior AI/ML engineer in Dubai to develop, deploy, and optimize ML models across NLP, computer vision, and automation. You will build AI-powered solutions, integrate services into enterprise apps, and drive generative, agentic, and MLOps workflows with governance and security in mind.

You will collaborate with business and engineering teams, mentor colleagues, and advance AI standards while navigating cost, performance, and scalability considerations.

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.
• 5+ years of software or AI/ML engineering experience.
• 3+ years building and operating production AI solutions.
• Strong Python programming and software engineering skills.
• Experience with enterprise AI governance, cloud platforms, and secure AI development.

Responsibilities

• Develop, deploy, and optimize machine learning and deep learning models.
• Build AI solutions for NLP, computer vision, predictive analytics, and intelligent automation.
• Integrate AI services into enterprise applications and internal platforms.
• Develop LLM-based applications, AI copilots, and RAG solutions.
• Implement prompt engineering, model evaluation, embeddings, and semantic search.
• Optimize model performance, scalability, and cost.
• Design and deploy autonomous and multi-agent AI systems.
• Develop agent orchestration, planning, reasoning, memory, and tool integration.
• Implement human-in-the-loop approvals and enterprise workflow automation.
• Build CI/CD pipelines for AI models and agents.
• Monitor AI systems for performance, drift, hallucinations, security, and reliability.
• Manage model versioning, deployment, observability, and lifecycle operations.
• Implement AI governance, model risk management, and Responsible AI practices.
• Maintain model documentation, validation, audit readiness, and compliance.
• Ensure fairness, traceability, maintainability, privacy, and regulatory adherence.
• Govern AI agent lifecycle, identity, access, and runtime controls.
• Implement guardrails, audit logging, and secure integration with enterprise systems.
• Protect AI solutions against prompt injection, data leakage, and adversarial attacks.
• Work with business, engineering, security, and architecture teams to deliver AI solutions.
• Mentor team members and promote AI engineering standards and best practice.

Skills

Python

Software Engineering

Enterprise AI Governance

Cloud Platforms

Secure AI Development

Education

Bachelor's or Master's degree in CS/AI/DS

Tools

PyTorch

TensorFlow

Scikit-learn

Hugging Face

LangChain

LangGraph

LlamaIndex

AutoGen

CrewAI

Semantic Kernel

Job description

• Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.
• 5+ years of software or AI/ML engineering experience.
• 3+ years building and operating production AI solutions.
• Strong Python programming and software engineering skills.
• Experience with enterprise AI governance, cloud platforms, and secure AI development.

Minimum

Work Experience

• Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.
• 5+ years of software or AI/ML engineering experience.
• 3+ years building and operating production AI solutions.
• Strong Python programming and software engineering skills.
• Experience with enterprise AI governance, cloud platforms, and secure AI development.

Responsibilities

AI & ML Engineering

• Develop, deploy, and optimize machine learning and deep learning models.
• Build AI solutions for NLP, computer vision, predictive analytics, and intelligent automation.
• Integrate AI services into enterprise applications and internal platforms.

Generative AI

• Develop LLM-based applications, AI copilots, and RAG solutions.
• Implement prompt engineering, model evaluation, embeddings, and semantic search.
• Optimize model performance, scalability, and cost.

Agentic AI

• Design and deploy autonomous and multi-agent AI systems.
• Develop agent orchestration, planning, reasoning, memory, and tool integration.
• Implement human-in-the-loop approvals and enterprise workflow automation.

AI Operations (MLOps / LLMOps / AgentOps)

• Build CI/CD pipelines for AI models and agents.
• Monitor AI systems for performance, drift, hallucinations, security, and reliability.
• Manage model versioning, deployment, observability, and lifecycle operations.

AI Governance & Responsible AI

• Implement AI governance, model risk management, and Responsible AI practices.
• Maintain model documentation, validation, audit readiness, and compliance.
• Ensure fairness, traceability, maintainability, privacy, and regulatory adherence.

Agent Governance & Security

• Govern AI agent lifecycle, identity, access, and runtime controls.
• Implement guardrails, audit logging, and secure integration with enterprise systems.
• Protect AI solutions against prompt injection, data leakage, and adversarial attacks.

Collaboration

• Work with business, engineering, security, and architecture teams to deliver AI solutions.
• Mentor team members and promote AI engineering standards and best practice

Technical Skills

AI & Machine Learning

• Machine Learning, Deep Learning, NLP, Computer Vision
• Predictive Analytics, Feature Engineering, Model Optimization

Generative AI

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Embeddings and Semantic Search
• Fine-tuning Foundation Models

Agentic AI

• Autonomous and Multi-Agent Systems
• Agent Orchestration
• Planning and Reasoning
• Memory Management
• Tool Calling
• Human-in-the-Loop (HITL)
• Model Context Protocol (MCP)

Frameworks & Libraries

• PyTorch
• TensorFlow
• Scikit-learn
• Hugging Face
• LangChain
• LangGraph
• LlamaIndex
• AutoGen
• CrewAI
• Semantic Kernel

Cloud & Infrastructure

• Microsoft Azure AI Foundry
• Azure Machine Learning
• AWS Bedrock
• Amazon SageMaker
• Google Vertex AI
• Docker
• Kubernetes
• Terraform

Data & Integration

• SQL
• PostgreSQL
• MongoDB
• Apache Spark
• Vector Databases
• REST APIs
• FastAPI

AI Operations

• MLflow
• Kubeflow
• Azure ML Pipelines
• GitHub Actions
• Azure DevOps

AI Monitoring & Observability