Job Purpose
The Technical Operational Excellence– Data & AI is responsible for designing, building, deploying, and operating advanced data platforms and AI/ML solutions that support enterprise analytics, automation, and data-driven decision-making. The role focuses on delivering scalable and reliable data pipelines, operationalizing AI/ML models and intelligent agents, and ensuring high standards of performance, governance, and operational excellence across modern data and AI ecosystems.
Key Accountabilities
Design, build, and operate large-scale data pipelines and platforms supporting analytics, business intelligence, and AI use cases. Develop, train, validate, deploy, and monitor AI/ML models and intelligent solutions aligned with business objectives. Build and manage AI and data pipelines using platforms such as Dataiku, Spark, Hadoop, and Kafka. Orchestrate and automate data and ML workflows using tools such as Apache Airflow and MLOps frameworks. Deploy and operate models and data workloads using APIs, containers, and modern runtime environments. Provide operational (L3) support for production data and AI platforms, including incident handling, troubleshooting, and performance optimization. Ensure data quality, model accuracy, scalability, and reliability across enterprise platforms. Implement and enforce data and AI governance, including lineage, security, explainability, and compliance. Collaborate with data architects, analysts, AI engineers, and business stakeholders to translate requirements into technical solutions. Document solutions, share knowledge, and support continuous improvement across data and AI operations. Research, evaluate, and apply emerging data and AI technologies, including advanced analytics and intelligent agent capabilities.

Minimum Qualifications
Bachelor’s degree in computer science, Information Technology, Data Science, or a related field.
Minimum Experience
6+ years of professional experience in data engineering, AI/ML engineering, or advanced analytics roles.

Job-Specific Skills
Proven experience working in enterprise-scale data platforms and/or production AI/ML environments. Hands-on experience supporting and operating critical data or AI workloads in production. Strong hands-on experience in data engineering and AI/ML solution delivery. Proficiency in Python and SQL for data processing and model development. Experience with Dataiku and MLOps tools for building and operationalizing ML pipelines. Solid knowledge of big data and data platform technologies such as Apache Spark, Hadoop, Kafka, and related ecosystems. Experience with workflow orchestration tools (e.g., Apache Airflow). Familiarity with ETL/ELT tools and data virtualization solutions (e.g., Informatica BDM, Denodo). Understanding of ML frameworks (e.g., Tensor Flow, PyTorch, scikit-learn). Experience deploying data and ML workloads via APIs, containers, and microservices. Knowledge of data governance, lineage, security, and model explainability concepts. Familiarity with cloud platforms (AWS, Azure, or GCP) is a plus. Exposure to advanced AI concepts such as LLMs, intelligent agents, or AI automation is an advantage. Ability to work effectively in cross-functional, enterprise environments.