Data engineer

Key Responsibilities include :

Data Pipeline Engineering : Design, build, and maintain ingestion, transformation, and storage pipelines using Azure Data Factory, Synapse Analytics, and Data Lake.

AI Data Enablement : Collaborate with AI Consultants to prepare and manage datasets for supervised, unsupervised, and synthetic learning workflows.

POC Support : Implement optimized data pipelines for Agentic AI POCs using realistic or synthetic data sources.

API & Microservices Development : Build RESTful APIs and Fast API-based microservices for data access and model interaction within Azure-hosted environments.

Model Deployment Support : Enable end-to-end deployment of AI models using

Azure Machine Learning, Azure Kubernetes Service (AKS), and Azure Dev Ops.

Data Governance & Compliance : Ensure data quality, lineage, and compliance with responsible AI and enterprise data standards.

Skills & Experience Required : Minimum Yo E : 5 Years

Data Engineering & Azure Ecosystem : Strong hands-on experience with

Azure Data Factory, Azure Synapse Analytics, Azure Data Lake, Azure Blob

Storage

Azure Machine Learning, Azure Dev Ops, Azure Kubernetes Service (AKS) Agentic AI Enablement : Experience supporting

GPT model fine-tuning, data preparation for LLMs, synthetic data generation

Vector / Graph DBs (neo4j), Azure AI Search, Lang Chain data loaders

Context engineering for agent memory and tool chaining

Responsible AI principles, model explainability, data privacy controls

Programming & Micro Services Architecture : Strong skills in

Python, Py Spark, SQL, Fast API (Pydantic, REST APIs)

ETL / ELT workflows, data modeling, schema design, feature engineering

Containerization using Docker / Kubernetes, and CI / CD automation

Soft Skills Ability to collaborate effectively with AI Consultants, Architects, and Business Analysts

Strong communication skills to explain data engineering concepts to non- technical stakeholders & ability to