BlackStone eIT
AI Engineer (Applied)
full timeseniorDubai, UAE5 months ago
42,000-65,000 AED/mo
AI & Machine LearningJob 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. - 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 Benefits - Paid Time Off - Performance Bonus - Training & Development
Requirements
- - 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
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
Skills Required
PythonTensorFlowPyTorchScikit-learnFastAPIDockerAWSGCPAzureMLOpsLLM APIs (Claude, GPT-4, open-weight models)Prompt engineeringJSON / JSON SchemaPydanticObservability and monitoringEvaluation metrics (precision/recall)Open-weight / sovereign model APIs (Falcon, Llama)