Full Stack Data Scientist (Azure AI Engineer)The Value Maximizer • Dubai, Dubai, United Arab Emirates

Full Stack Data Scientist (Azure AI Engineer)

The Value Maximizer • Dubai, Dubai, United Arab Emirates

9 منذ أيام

الوصف الوظيفي

Overview

Location : Dubai

Experience : 8+ years (Data Science / AI Engineering / Applied ML)
Job Type : Full-time

Job Summary

We are looking for a highly capable Full Stack Data Scientist / Azure AI Engineer who can build end-to-end AI products : data + ML / DL / CV models + Agentic workflows + APIs + UI + scalable deployment on Kubernetes (AKS). The role requires deep expertise in the Azure AI ecosystem (Azure Machine Learning, Azure AI Foundry, Azure AI Search) and strong hands-on experience building AI agents using LangChain, LangGraph, and / or Microsoft Agent Framework, with Langfuse for tracing, evaluation, and observability. The ideal candidate has shipped production systems with measurable business impact and can operate them reliably through strong MLOps / LLMOps practices.

Responsibilities

End-to-End AI Product Delivery : Own delivery from problem definition → architecture → development → deployment → monitoring → iterative improvements

Translate business needs into robust AI solutions with clear KPIs, timelines, and measurable outcomes

Build AI applications that are secure, scalable, maintainable, and production ready

AI Agents & Agentic Workflows (Must-Have)

Design, implement, and orchestrate AI agents capable of planning, tool use, function calling, retrieval, and multi-step execution

Build agent systems using : LangChain, LangGraph, Microsoft Agent Framework

Implement agent patterns : routing, task decomposition, multi-agent collaboration, memory, verification, retries / fallbacks, and human-in-the-loop approvals

Apply security & safety : prompt-injection defenses, tool permissioning, grounding / citations, policy checks, and audit logs

LLMOps / Observability / Evaluation (Langfuse)

Implement Langfuse for prompt and trace logging, latency / cost monitoring, dataset-based evaluation, regression testing, quality gates, and feedback loops

Establish evaluation frameworks for RAG / agents : retrieval metrics, answer quality, hallucination checks, and guardrail effectiveness

Azure Machine Learning & MLOps (Must-Have) : build / operate ML workflows, model registry and endpoints; CI / CD for model + app releases and MLOps practices

Azure AI Foundry & Azure AI Search (Must-Have) : GenAI solutions, RAG pipelines with ingestion / indexing, vector and hybrid search, citations, metadata-based access control, indexing automation

ML / DL & Computer Vision (Strong Requirement) : develop and deploy CV solutions (classification, detection, segmentation, OCR, anomaly detection); experimentation and productionize CV pipelines with monitoring

Backend / API Engineering (FastAPI + Node.js) : build production APIs, service orchestration and secure patterns

Frontend Engineering (React) : modern UIs for AI applications, support streaming, citations, memory, feedback, analytics

Kubernetes / AKS Deployment & Operations : containerize with Docker, deploy on Kubernetes, scaling, secrets / config, ingress, reliability; monitoring with Azure Monitor / App Insights

Required Skills and Qualifications : AI-102, DP-100, LangChain / LangGraph / Microsoft Agent Framework, Langfuse, Azure ML / Foundry / Search, Key Vault, Storage, App Insights

Programming : Python; API development with FastAPI; Node.js for services / integrations

Frontend : React for production UI development

ML / DL / CV : Hands-on depth in ML / DL and Computer Vision

Deployment : Docker + Kubernetes / AKS

Data : Strong SQL; experience with structured + unstructured data

Proven Experience (Non-Negotiable) : end-to-end delivery of AI applications in production with measurable impact

Preferred Qualifications

Experience in real estate / construction domain AI use cases (valuation, forecasting, risk, customer support automation)

Exposure to graph databases (e.g., Neo4j) and vector search / vector databases for AI applications

Extra certifications (nice-to-have) : Azure Fundamentals (AZ-900), Azure Developer (AZ-204), Kubernetes (CKA / CKAD), Databricks ML

What Success Looks Like

Delivered production-grade AI solutions end-to-end : data → model → agentic workflow → API → UI → AKS deployment → monitoring

Established strong LLMOps with Langfuse : traceability, evaluation, cost controls, and reliability improvements

Built reliable, secure, observable systems with measurable business impact (time saved, accuracy gains, automation rate, cost reduction)

Demonstrated strong ownership from POC to production and post-launch iteration

#J-18808-Ljbffr

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