Top Tech Jobs & Startup Jobs

Build and deploy machine learning and NLP models, develop end-to-end modeling pipelines, use BigQuery ML for data processing and training, prototype generative AI solutions, and mentor junior team members. The role requires hands-on experience with ML engineering, NLP, LLM concepts, embeddings, prompting, and exposure to agentic systems.

Oversee project planning, execution, budgets, timelines, resources, risks, quality, and delivery. Lead project teams, coordinate stakeholders, provide status updates, resolve issues, and maintain project documentation and reporting in SharePoint. The role requires strong project management, leadership, communication, prioritization, and multitasking skills. Knowledge of Windows and Unix servers is advantageous.

Develop and support backend software in Azure environments using Java and Python APIs, microservices, Snowflake, PostgreSQL, Kafka, and AI tools. Provision infrastructure with Terraform and apply distributed-system design, secure development, observability, and production-support practices. The role supports healthcare projects in Ireland on a hybrid schedule.

Designs and implements AI-driven solutions across the software development life cycle using the GitHub ecosystem. Responsibilities include architecting and tuning coding agents, building evaluation frameworks, governing prompt libraries, integrating AI with CI/CD pipelines and MCP servers, and embedding agentic workflows into enterprise engineering processes. The role requires advanced programming expertise, strong AI architecture knowledge, and experience applying generative AI within complex enterprise SDLC environments.

Design and develop cloud-native applications for a finance AI platform using Java and/or Python. Build microservices, APIs, event-driven solutions, and Kubernetes-based containerized workloads. Implement CI/CD pipelines and DevOps practices while collaborating with architects and product teams. Contribute to AI-enabled applications, including LLM and RAG integrations, within established engineering standards and delivery frameworks.

Designs, deploys, and manages highly available Kubernetes platforms on AWS EKS. Automates infrastructure with Terraform or CloudFormation, builds CI/CD pipelines, manages networking and security, monitors performance with Prometheus, Grafana, and CloudWatch, optimizes cost and resources, troubleshoots platform issues, and supports cloud-native adoption.

Designs Azure cloud architectures for application and database migration, modernization, connectivity, identity, governance, and platform capabilities. Leads transformation from monolithic systems to modular, cloud-native ecosystems while improving agility, resilience, security, compliance, performance, and cost efficiency. Requires enterprise application experience, technical leadership, architecture and design expertise, stakeholder management, documentation, and at least seven years of relevant experience. Azure transformation, DevOps, Fabric, Power BI, data lakehouse, AI/ML, and API-led integration experience are desirable.

Provides technical leadership for enterprise application and database modernization. The role guides migration to Microsoft Azure, transformation of monolithic systems into modular cloud-native architectures, and integration across complex business applications. Responsibilities include producing reference implementations, improving engineering quality, strengthening security and governance, influencing stakeholders, leading transformation initiatives, translating business objectives into technology solutions, and supporting documentation and knowledge transfer.

Develop full-stack applications using Java Spring Boot, ReactJS, APIs, and microservices. Design and build APIs, write Swagger documentation, create low-level designs, conduct code reviews, mentor team members, perform unit and integration testing, develop test automation scripts, and deploy code using Azure or GCP platforms and CI/CD pipelines while managing repositories.

Own and evolve EMEIA infrastructure across VM-based and managed container environments. Design and build a Kubernetes-based platform, migrate existing workloads, manage networking, access controls, certificates, cloud infrastructure, RHEL systems, and infrastructure-as-code. Establish deployment and observability practices, troubleshoot incidents, maintain reliability and documentation, and support rapidly developed AI-assisted applications. Collaborate with developers, data teams, analysts, vendors, and global stakeholders.