AI Expert for Plant Data Model (PDM Project)

Job Profile: AI Expert for Plant Data Model (PDM Project)

Job Summary

We are seeking a highly skilled AI Expert to design, build, and operationalize AI/ML solutions that deliver measurable business outcomes. The ideal candidate combines deep machine learning expertise with strong software engineering practices, MLOps proficiency, and stakeholder collaboration. This role will lead end-to-end projects—from problem framing and data acquisition to model development, deployment, monitoring, and continuous improvement—while ensuring compliance with security, privacy, and governance policies.

Key Responsibilities

· Partner with business stakeholders to translate strategic objectives into AI use cases with clear KPIs and ROI.

· Own the full ML lifecycle: data sourcing, feature engineering, model selection, training, evaluation, and production deployment.

· Implement robust MLOps: CI/CD for ML, model registry, automated testing, reproducible pipelines, and monitoring (drift, performance, fairness).

· Design scalable data/feature pipelines using best practices for data quality, lineage, and governance.

· Develop models across domains (e.g., predictive analytics, time series, NLP, computer vision, recommender systems).

· Optimize models for efficiency and cost across CPU/GPU, leveraging quantization, distillation, or serving optimizations.

· Deploy and maintain models on cloud and on-prem platforms; orchestrate with containers and microservices (Docker/Kubernetes).

· Establish and enforce responsible AI practices: privacy-preserving methods, bias detection/mitigation, explainability, and secure handling of data.

· Produce high-quality documentation: design docs, runbooks, experiment logs, and user guides; present results to technical and non-technical audiences.

· Mentor engineers and analysts; contribute to standards, templates, and reusable components within the AI platform.

Minimum Qualifications

· Bachelor’s degree in computer science, Data Science, Engineering, Mathematics, or related field (or equivalent experience).

· 5+ years building and deploying ML models in production environments.

· Strong programming skills in Python and experience with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).

· Hands-on experience with data processing frameworks (e.g., Pandas, Spark), SQL/NoSQL databases, and feature stores.

· Proficiency in MLOps tooling (e.g., MLflow, DVC, Kubeflow, Airflow) and CI/CD for ML.

· Experience deploying to cloud platforms (e.g., Azure Machine Learning, AWS SageMaker, GCP Vertex AI) and container orchestration (Docker/Kubernetes).

· Solid understanding of statistics, experiment design (A/B testing), and model validation techniques.

· Excellent communication and stakeholder management skills; ability to articulate complex concepts clearly.

Preferred Qualifications

· Master’s or PhD in a quantitative field.

· Experience with NLP (transformers, LLMs, RAG) and computer vision (CNNs, detection/segmentation).

· Exposure to time-series forecasting, optimization, and reinforcement learning for industrial use cases.

· Experience with Azure ecosystem (Azure ML, Databricks, Data Factory, Synapse) and enterprise integration patterns.

· Familiarity with privacy-preserving techniques (differential privacy, federated learning) and model interpretability (SHAP, LIME).

· Track record of delivering AI solutions in manufacturing, energy, or operations (e.g., predictive maintenance, process optimization).

Technical Stack & Tools

· Languages: Python (preferred), SQL; familiarity with C++/Java is a plus.

· ML/AI: scikit-learn, TensorFlow, PyTorch, XGBoost, Hugging Face; experiment tracking (MLflow).

· Data: Pandas, Spark, Delta/Parquet; data versioning (DVC); feature stores.

· Pipelines & Orchestration: Airflow, Prefect, Kubeflow; Docker/Kubernetes for serving.

· Monitoring & Observability: Prometheus/Grafana; model drift/performance monitoring tools.

· DevOps: Git, GitHub/GitLab, CI/CD, testing frameworks (pytest).

· Cloud: Azure (preferred), AWS, GCP; serverless and managed ML services.

Core Competencies & Soft Skills

· Business acumen with the ability to prioritize use cases based on value and feasibility.

· Structured problem solving and hypothesis-driven experimentation.

· Clear written and verbal communication, storytelling with data, and visualization skills.

· Collaboration and mentorship across cross-functional teams (engineering, operations, compliance).

· Ownership mindset with a focus on reliability, safety, and continuous improvement.

AI Governance, Security & Compliance

· Adhere to corporate data governance policies, security standards, and regulatory requirements (e.g., GDPR where applicable).

· Implement role-based access controls, encryption, secure secrets management, and audit trails across the ML stack.

· Ensure models meet fairness, transparency, and explainability guidelines; conduct regular risk assessments and reviews.

Success Criteria & KPIs

· Cycle time from ideation to production (weeks).

· Model performance against business KPIs (e.g., accuracy, precision/recall, uplift, cost savings).

· Operational reliability (SLA adherence, downtime, incident rate).

· Adoption and user satisfaction for AI-enabled products and workflows.

Compliance metrics (governance checks passed, audits completed).

Job Type: Full-time

Pay: From QAR15,000.00 per month

Education:

• Bachelor's (Required)

Experience:

• AI Expert for Plant Data Model (PDM Project) : 3 years (Required)

Language:

• Arabic (Required)
• English (Required)

License/Certification:

• degree in computer science, Data Science (Required)

Location:

• Doha (Required)