Top Tech Jobs & Startup Jobs

Supports client engagements and manages workstreams or smaller projects across digital transformation, technology, data and AI, commerce, and business transformation initiatives. Maintains project plans, schedules, RAID logs, status reports, governance documentation, and delivery dashboards. Coordinates multidisciplinary teams, facilitates meetings and Agile ceremonies, monitors risks and dependencies, supports stakeholder communication and change management, and contributes to process improvement, proposals, and project planning.

Lead client data governance implementation by identifying stakeholders and governance gaps, defining ownership and decision forums, facilitating business-technical workshops, and embedding governance into daily operations. The role translates ambiguous challenges into actionable approaches, develops reusable templates and playbooks, and collaborates with data, analytics, architecture, privacy, and security specialists. It requires stakeholder engagement, data governance or related experience, data platform understanding, and strong communication skills.

Lead client data governance implementation by identifying governance gaps, defining ownership roles, facilitating stakeholder workshops, translating ambiguous challenges into actionable approaches, and embedding governance into daily operations. Develop reusable templates, playbooks, and implementation accelerators while collaborating with data, analytics, architecture, privacy, security, and business consulting specialists.

Designs and governs scalable AI architecture across cloud, data, and application environments. Responsibilities include defining architectural standards, advising on AI solution design, overseeing integrations and deployments, monitoring infrastructure performance and costs, coordinating technical stakeholders, supporting specifications and acceptance testing, and mitigating technical risks. The role requires hands-on experience with cloud platforms, AI/ML systems, DevOps, data architecture, containers, CI/CD, infrastructure as code, and monitoring.

Lead the architecture, integration, scalability, reliability, and governance of enterprise AI systems. Establish technical standards, advise on AI solution design, oversee specifications and testing, manage cloud infrastructure performance and costs, coordinate cross-functional teams, and mitigate technical risks. The role requires strong experience in DevOps, cloud architecture, AI tooling, data platforms, infrastructure as code, CI/CD, containers, and major cloud providers.

Support the development, testing, deployment, and improvement of AI solutions, including LLM applications, RAG pipelines, prompts, AI agents, and orchestration workflows. Prepare data and knowledge foundations, research emerging models and frameworks, translate business needs into AI use cases, monitor solution performance, and document methodologies. Collaborate with senior technical professionals, data teams, and business stakeholders in an international environment.

Design and support scalable AI infrastructure, applications, data pipelines, and cloud-based solutions. Collaborate with technical and business stakeholders, contribute to architecture and documentation, support deployments and testing, monitor performance and costs, troubleshoot issues, and help manage technical risks. The role requires cloud, DevOps, data engineering, infrastructure-as-code, containerization, CI/CD, monitoring, and AI/ML platform experience.

Supports the development, testing, deployment, and monitoring of AI solutions. Responsibilities include preparing data and knowledge foundations, building RAG pipelines, testing prompts and context strategies, researching AI models and tools, contributing to AI agents and orchestration workflows, translating business needs into use cases, and documenting solutions. The role collaborates with data, AI, technical, and business teams while continuously improving AI application performance and reliability.

Lead a data engineering team and technical roadmaps for client data platforms. Design architectures and deliver scalable ETL/ELT pipelines, data lakes, warehouses, analytics products, APIs, dashboards, and data insights using Python, Spark, and Azure. Ensure engineering excellence, automated testing, governance, and alignment with digital standards. Collaborate with data scientists, stakeholders, and project teams, while coaching team members and evaluating business needs.

Design, build, and refine AI solutions from prototype through production. Develop context layers using knowledge graphs, ontologies, governance, data lineage, and RAG architectures. Apply prompt, context, memory, and harness engineering; translate business challenges into AI workflows; contribute to agentic systems and orchestration architectures; evaluate emerging AI tools; and document reusable methodologies and standards.