Top Tech Jobs & Startup Jobs

Design, build, migrate, operate and govern hybrid cloud and on-prem infrastructure. Architect and deliver cloud transformations, implement virtualization, container platforms, automation, and resilience strategies.

Deploy and adapt existing ML models, integrate processing pipelines into Airflow and APIs, transform data from MinIO and PostgreSQL, optimize PySpark for monthly alerts, and manage ML workflow dependencies, sequencing, stages, and deployments.

Design and define end-to-end scalable, resilient AI architectures (data pipelines, model training/serving, retrieval, monitoring, governance), collaborate with delivery teams, contribute to proposals, mentor engineers, and evaluate emerging AI tools and technologies.

Lead design, build, and deployment of production-grade agentic AI systems for enterprise clients. Architect multi-agent orchestration, RAG pipelines, multi-LLM integrations, and programme-scale LLMOps (eval, observability, safety, cost). Lead client engineering engagements, publish reusable patterns and standards, and manage an engineering team to deliver reliable, observable agentic solutions.

Lead complex, end-to-end technology sales cycles for financial services, engage C-level clients, craft value propositions across cloud, data/AI, core banking and cybersecurity, shape commercial models, orchestrate deal teams, build partner offerings, and drive pipeline and origination initiatives to close multi-million deals.

Design and implement Oracle Cloud Record to Report solutions for clients. Facilitate requirements and configuration workshops, recommend business process improvements, lead solution design, testing, deployment, and integration activities, and guide small project teams. The role also includes planning deliverables, advising stakeholders, applying Oracle methodologies, supporting business development, and maintaining knowledge of Oracle features and industry trends.

The posting provides no specific responsibilities or qualifications beyond the Data Science Manager title. The role appears associated with Accenture’s Data and AI services and resources practice.

Supports Accenture’s Data & AI practice by applying analytics, statistical methods, predictive modeling, and AI-based solutions for SaaS and PaaS clients. The role contributes to data-driven decision-making, identifies growth opportunities, develops analytic capabilities, and executes client delivery projects with global onsite and offshore teams.

Lead design and delivery of AI-powered system transformations for enterprise clients across the full lifecycle: requirements, design, development, testing, migration, project management, and operations. Develop and deploy generative AI (LLM/RAG/agent) solutions, collaborate with cross-functional and global teams, and improve internal system integration and operational practices.

エンタープライズ向けに生成AIやクラウドを活用した業務変革を推進するポジション。要件定義〜設計・開発・テスト・移行・運用設計までのシステムライフサイクルとプロジェクト管理、社内外チームやグローバル連携での価値創出を担当します。