AI Engineer (Applied)

BlackStone eIT; Dubai; On-site; AED 120,000 - 150,000; Full time

Benefits offered by this job

Paid Time Off

Performance Bonus

Training & Development

Job summary

A tech company located in Dubai is seeking an experienced AI Engineer (Applied) to enhance its dynamic team. The role involves applying AI techniques to solve real-world challenges, focusing on deployment and improvement of AI models in production environments. The ideal candidate will have over 5 years of experience in AI system development, strong programming skills in Python, and familiarity with AI frameworks like TensorFlow or PyTorch. Benefits include paid time off and performance bonuses.

Qualifications

• 5+ years of experience applying AI techniques in production.
• Experience with AI model deployment and maintenance.
• Strong understanding of data preprocessing and model evaluation.

Responsibilities

• Implement automated classification for data pipelines.
• Build production AI agents for operational workflows.
• Define data contracts for the agentic platform.

Skills

AI and machine learning techniques

Programming languages (Python)

AI/ML frameworks (TensorFlow, PyTorch)

Collaborative problem-solving

Cloud platforms (AWS, GCP, Azure)

Prompt engineering

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, or AI

Tools

FastAPI

Docker

Job description

BlackStone eIT is seeking a talented and pragmatic AI Engineer (Applied) to join our dynamic team. In this role, you will focus on applying artificial intelligence techniques to solve real‑world problems and deliver impactful solutions that drive business value. You will collaborate closely with product teams, data scientists, and software engineers to deploy AI models into production environments and continuously improve their performance.

The ideal candidate has hands‑on experience with end‑to‑end AI system development and a strong focus on practical applications and scalability. You will work on diverse projects that require innovative AI solutions, from natural language processing to computer vision and predictive analytics.

Responsibilities

• Data classification automation: implementing automated classification to remediate current failures, embedding classification into data pipelines alongside the Governance Lead
• Operational AI agents: building production agents on top of the agentic platform — going beyond the sample agents the external partner delivers into real operational workflows
• Agentic platform data contracts: defining what data the platform needs, in what format, with what quality guarantees — working with the Principal AI Engineer
• AI service implementation: FastAPI service around LLM APIs with versioned prompt templates
• Classification and briefing prompts: structured prompts returning validated JSON with tags, confidence levels, source attribution
• Prompt versioning: templates in configuration, editable without code changes
• Observability: every LLM call logged with input hash, model version, output, latency, token count
• Fallback logic: graceful degradation when LLM APIs are unavailable
• Quality evaluation: running precision/recall evaluations against human reviewer samples, reporting results, iterating prompts

Qualifications

• 5+ years of experience applying AI and machine learning techniques in a production environment.
• Strong proficiency in programming languages such as Python and familiarity with AI/ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Experience with deploying and maintaining AI models at scale.
• Good understanding of data preprocessing, feature engineering, and model evaluation.
• Background in statistics, mathematics, or computer science.
• Ability to collaborate effectively with cross-functional teams and translate business needs into applied AI solutions.
• Excellent problem‑solving skills and a practical, solution‑oriented mindset.
• Experience with cloud platforms (AWS, GCP, Azure) and MLOps practices is a plus.
• Bachelor’s or Master’s degree in a relevant field such as Computer Science, Data Science, or AI.
• LLM APIs (Claude, GPT-4, open-weight models) — structured output, JSON mode, system prompts
• Prompt engineering for classification — zero-shot and few-shot
• Python — async API calls, retry logic, exponential backoff
• LLM evaluation — precision/recall, human-AI agreement scoring
• Structured output — JSON schema enforcement, Pydantic validation
• Open-weight / sovereign model APIs (Falcon, Llama, or equivalent)
• Token budgeting and context window management
• AI observability — output quality monitoring, anomaly detection
• FastAPI and Docker

Benefits

• Paid Time Off
• Performance Bonus
• Training & Development