Data AI Engineer; CLT

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).
• Open Shift/Kubernetes container technologies.
• CI/CD pipelines, Dev Ops practices, and Infrastructure as Code.