Data and AI/ML Engineering

Data Engineering ArchitectureDesign and lead the companys modern data platform architecture.Build scalable systems for data ingestion, processing, transformation, and storage.Enable reliable and governed data access for analytics, ML models, and AI applications.Build and manage large-scale data pipelines and ETL/ELT systems.Implement modern architectures such as: Data Lake, Data Warehouse, Lakehouse architectures.Ensure scalability, reliability, and performance of data infrastructure.AI / Machine Learning EngineeringBuild infrastructure for training, deploying, and monitoring ML models.Develop scalable ML pipelines and feature engineering systems.Enable product teams to embed AIpowered capabilities into applications.Generative AI & LLM SystemsDrive adoption of Generative AI technologies across products.Design systems using large language models (LLMs) for intelligent automation and datadriven applications.Build architectures for: LLM integration, RetrievalAugmented Generation (RAG), Vector search systems, AI agents and copilots.Evaluate and integrate modern GenAI frameworks and tooling.MLOps & AI InfrastructureBuild and maintain infrastructure for: Model training, Model versioning, Model deployment, Monitoring and observability, Experimentation frameworks.Establish MLOps best practices for reliable production ML systems.Data Governance & QualityAI Adoption Across ProductsPartner with product engineering teams to enable: Predictive analytics, Recommendation systems, Intelligent automation, AIdriven decision systems, GenAIpowered product features.Leadership ResponsibilitiesBuild and lead the Data & AI/ML Engineering Pod.Mentor data engineers, ML engineers, and AI engineers.Define the technical roadmap for data and AI systems.Establish best practices for data engineering, ML systems, and AI infrastructure.Drive adoption of AI and GenAI capabilities across engineering teams.Data PlatformsData pipelines and distributed data processing.Data lake / lakehouse architectures.Streaming and realtime data processing.Largescale analytics platforms.Machine Learning SystemsML pipelines and feature stores.Model training and deployment.ML model monitoring and lifecycle management.Generative AILarge Language Models (LLMs).RetrievalAugmented Generation (RAG).Vector databases and embedding systems.AI agents and copilots.Prompt engineering and LLM orchestration frameworks.Required QualificationsExperience leading Data Engineering or AI/ML Engineering teams.Strong background in largescale data systems.Experience building production machine learning systems.Good understanding of Generative AI and LLMbased applications.Experience designing scalable data and AI platforms.Preferred QualificationsExperience building AIpowered enterprise platforms.Experience integrating GenAI features into production systems.Experience with largescale data environments.Familiarity with geospatial or location intelligence data.Leadership ExpectationsDefine the data and AI strategy for the company.Build scalable data platforms and AI infrastructure.Enable product teams to leverage data, ML, and GenAI capabilities.Drive innovation through AIpowered product development. .