AI Engineer

As part of a global team, you will support organizations by transforming bold ideas into real-world impact. Collaborating with talented colleagues worldwide, you will help clients rethink how they use technology to achieve growth, innovation, and a more sustainable future. From strategy formulation to designing and developing cutting-edge solutions in AI, cloud, and data, you will be part of projects that solve meaningful challenges and create lasting value for businesses, communities, and your own career.

Key Responsibilities :

AI & Model Development

• Design, train, and deploy predictive machine learning models and LLM-powered applications, including Retrieval-Augmented Generation (RAG) systems.
• Leverage Azure Machine Learning and Azure AI Foundry to build scalable and production-ready AI solutions.

Data Integration & Collaboration

• Partner with data engineering teams to ingest and process high-volume datasets (Parquet, CSV, text) from Azure Data Lake Storage (ADLS) and Azure Synapse Analytics.
• Ensure seamless integration between data pipelines and AI workflows.

Application Architecture

• Develop serverless orchestration layers using Azure Functions to connect AI models with downstream applications and APIs.
• Support real-time and batch inference use cases.

Search & Storage Optimization

• Use Azure AI Search to enable low-latency retrieval for AI outputs, embeddings, and metadata.
• Contribute to efficient data and metadata storage strategies.

Operational Excellence & MLOps

• Implement MLOps best practices, including model versioning, monitoring, and lifecycle management.
• Integrate AI workflows into CI/CD pipelines to enable automated testing and deployment.

Azure AI & Cloud Stack:

• Hands-on experience with Azure Machine Learning, Azure AI Foundry, and Azure OpenAI Service.

Data & Analytics:

• Strong understanding of Azure Data Lake Storage (ADLS) and Azure Synapse Analytics.
• Experience working with Parquet files and large-scale, enterprise data environments.

Programming & Frameworks:

• Proficiency in Python for AI/ML development.
• Working knowledge of Scala and Apache Spark, aligning with enterprise ETL platforms.

Backend & Storage:

• Experience building serverless solutions using Azure Functions.
• Familiarity with Azure Cosmos DB or other NoSQL data stores.

DevOps & Automation:

• Knowledge of CI/CD tooling such as Azure DevOps or GitHub Actions to automate AI and ML workflows.

Preferred Qualifications:

• Experience in the Insurance or Financial Services domain
• Ability to extract actionable insights from complex customer and transactional datasets
• Hands-on experience designing and implementing RAG-based AI systems
• Familiarity with distributed data processing using Azure Databricks