Data AI Engineer (CLT)

Description

Design, develop, and implement scalable data integration platforms and AI-enabled solutions leveraging modern cloud, data, and machine learning technologies. This role requires strong expertise in data engineering, real-time and batch processing, AI/GenAI application development, and deployment of models within containerized environments. The engineer will collaborate with global teams to build resilient data pipelines, AI-driven solutions, and production-grade platforms while ensuring security, compliance, scalability, and operational excellence in highly regulated environments.

8+ years of experience in Data Engineering, Software Engineering, AI/ML Engineering, or related technology roles.

Strong programming skills in Scala or Python and SQL, with experience building enterprise-grade applications and data solutions.

Hands-on experience designing, developing, and deploying data integration solutions using Informatica, Hadoop, Oracle, and related technologies.

Experience building AI/GenAI applications utilizing LLMs, RAG architectures, vector databases, and model inferencing frameworks.

Experience developing and supporting real-time, near real-time, and batch processing solutions.

Understanding of software engineering best practices, design patterns, testing, and production support processes.

Strong analytical, problem-solving, and quantitative skills with the ability to quickly learn new technologies and platforms.

Experience working within Agile delivery teams and collaborating across multiple business and technology organizations.

Ability to coordinate with development, deployment, and production support teams to ensure successful delivery into testing and production environments.

Strong communication, presentation, stakeholder management, and leadership skills.

Experience working with globally distributed teams across multiple time zones.

Experience with AI/ML concepts, Generative AI, Large Language Models (LLMs), Agentic AI, and Prompt Engineering.

Retrieval Augmented Generation (RAG) architecture and vector databases.

Understanding of MLOps, model monitoring, and AI governance.

Data integration and ETL/ELT development using Informatica, Hadoop ecosystem, and Oracle SQL.

Experience with distributed data processing technologies such as Spark and Hive.

Design and implementation of real-time, near real-time, and batch data pipelines.

Data modeling, metadata management, data quality, and data governance concepts.

Experience with API integration and event-driven architectures (Kafka preferred).

OpenShift/Kubernetes container technologies.

CI/CD pipelines, DevOps practices, and Infrastructure as Code.