Senior Data & AI Architect

Job Brief: VentureDive is seeking a Senior Data & AI Architect to lead the design and delivery of scalable, secure, and high-performance data and AI ecosystems across cloud and hybrid environments. You will translate complex business requirements into robust architecture strategies — from data pipelines to production-grade intelligent systems powering analytics, automation, and innovation across VentureDive's portfolio and client engagements. VentureDive Overview: Founded in 2012 by veteran technology entrepreneurs from MIT and Stanford, VentureDive is the fastest-growing technology company in the region that develops and invests in products and solutions that simplify and improve the lives of people worldwide. We aspire to create a technology organization and an entrepreneurial ecosystem in the region that is recognized as second to none in the world. Key Responsibilities Architecture & AI/ML - Design end-to-end Data & AI solutions across cloud, on-premises, and hybrid environments. - Build data ingestion, transformation, and storage pipelines for all data types. - Architect data lakehouse, data mesh, and event-driven systems aligned to scale requirements. - Design AI/ML pipelines: feature engineering, training, deployment, monitoring, and retraining. - Architect Agentic AI applications — RAG pipelines, tool-using agents, and LLM orchestration workflows. - Integrate Generative AI, NLP, and computer vision services into enterprise applications. Platform, Governance & Leadership - Leverage platforms such as Databricks, Snowflake, BigQuery, Synapse, Vertex AI, and SageMaker. - Establish MLOps and DataOps practices for continuous delivery and monitoring of models. - Implement data governance, model governance, and Responsible AI frameworks. - Define Data & AI strategy, architecture blueprints, and technology roadmaps with stakeholders. - Mentor engineers and data scientists on best practices in architecture and cloud-native design. - Evaluate and recommend emerging AI and LLM technologies for enterprise use cases. Required Experience & Qualifications - 10+ years in data engineering, data architecture, or AI solution design; 3+ years in a senior/principal architect role. - Hands-on experience across the modern data stack: Pipelines: Apache Spark, Airflow, dbt, Kafka, Flink - Storage: Snowflake, Delta Lake, BigQuery, Redshift, Synapse - AI/ML: TensorFlow, PyTorch, scikit-learn, MLflow, Azure ML, SageMaker, Vertex AI - Proven track record deploying ML models at scale — batch, streaming, and real-time. - Deep understanding of data lakehouse and data mesh architectures in enterprise contexts. - Experience with governance, lineage, access control, and security frameworks for data and AI. - Exceptional communication skills; able to convey complex architecture to engineering, product, and executive audiences. Preferred Skills - Generative AI, LLMs, vector databases, and RAG architectures (LangChain, LlamaIndex, or similar). - MLOps tooling: MLflow, Kubeflow, Vertex Pipelines, or Azure ML Pipelines. - DataOps and CI/CD practices for data and AI workflows. - BI/visualization tool integration: Power BI, Tableau, or Looker. - Real-time AI: anomaly detection, predictive maintenance, forecasting, or conversational AI. - Experience leading enterprise data modernization or AI transformation programs. - Compliance frameworks: GDPR, ISO 27001, NIST AI RMF, or sector-specific standards.

(تازہ ترین نوکریاں، سلیبس اور ٹیسٹ کی تیاری کی مکمل معلومات)