SUMMARY:
We are seeking an experienced Data & AI Engineer to design, build, and optimize data pipelines, AI/ML workflows, and cloud-based solutions. The ideal candidate will have deep expertise in Python, SQL, cloud data tools, and AI/ML frameworks, with the ability to deploy scalable and responsible AI solutions. This role is highly cross-functional, requiring both technical leadership and collaboration with business teams to deliver advanced analytics and AI-driven insights.

KEY RESPONSIBILITIES:

• Build and maintain automated data pipelines for data cleaning, transformation, and AI workflows.
• Develop, train, deploy, and monitor machine learning models using frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Manage data lakes/warehouses with optimization for scale, cost, and performance.
• Implement full MLOps lifecycle including versioning, retraining, and monitoring of models.
• Integrate APIs into data workflows and support backend data structures for BI dashboards.
• Ensure compliance with data governance, privacy, and responsible AI practices.
• Troubleshoot pipeline/model issues and implement optimization for high-volume workloads.
• Collaborate with cross-functional teams to support AI experimentation and proof-of-concepts.
• Maintain metadata, data dictionaries, and documentation for auditing and knowledge transfer.
• Contribute to workshops, knowledge-sharing, and governance sessions to enhance institutional AI maturity.

QUALIFICATIONS:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Artificial Intelligence, or Machine Learning.

EXPERIENCE:

• Minimum of 7 years in data engineering, AI, or advanced analytics, including at least 2 years of hands-on AI/ML project delivery.
• Proven expertise in Python, SQL, cloud data platforms (Azure, AWS), and orchestration tools (Airflow, Talend, Spark).
• Practical knowledge of AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Hands-on MLOps experience with model lifecycle management.
• Preferred: Experience with responsible AI, model explainability, and AI governance.

Must-Have Skills

1. Advanced Python programming for data engineering & AI model development.
2. Strong SQL skills for relational databases and warehouses.
3. Proven experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
4. Advanced ETL/ELT pipeline design using Airflow, Talend, or Spark.
5. Cloud data services expertise (Azure, AWS) for scalable AI deployments.
6. MLOps lifecycle management (model deployment, versioning, retraining).
7. Data governance knowledge (GDPR, compliance, responsible AI).
8. AI algorithm expertise (supervised, unsupervised, reinforcement learning).
9. Data lake/warehouse optimization for cost and performance.
10. Strong troubleshooting and optimization skills for AI models and pipelines.