Top Python Jobs in Dublin

The role involves researching cybersecurity threats, vulnerability analysis, developing automated tools, and collaborating with teams to provide technical expertise and publish findings.

Conduct exploratory product and marketplace analyses using SQL and statistical programming, define business metrics, build dashboards and scalable data assets, and guide A/B testing. Partner with Product, Engineering, and Data teams to generate actionable insights, improve experimentation and data architecture, support product strategy, and communicate complex findings to executives.

Designs, builds, and operates Toast’s distributed compute and storage infrastructure on AWS. Responsibilities include managing highly available relational and nonrelational databases, scalable compute clusters, containerized environments, streaming pipelines, infrastructure automation, observability, performance engineering, reliability, and cloud cost optimization. The role supports low-latency, high-throughput products across Toast’s platform and requires hybrid in-office work two days per week.

Designs, builds, and operates Toast’s distributed compute and storage infrastructure on AWS. Responsibilities include managing relational and nonrelational databases, improving availability and latency, automating infrastructure with Terraform and programming languages, operating containerized environments, implementing observability and SRE practices, supporting AI/ML infrastructure, and optimizing cloud costs and performance.

Lead pre-sales architecture for MongoDB Atlas and specialize in application search, including lexical and semantic search. Advise customers on scalable systems, conduct technical discovery, demos, proofs of value, presentations, sizing, and documentation, and support complex enterprise deal cycles. Enable other solution architects, mentor colleagues, relay customer feedback to Product Management, and influence Vector Search and AI/ML direction. The role is remote in Barcelona, Dublin, or London and requires approximately 25% travel.

Develop and maintain Ripple’s multi-region, multi-provider infrastructure and platform services. Build automation, monitoring, instrumentation, CI/CD pipelines, and lifecycle services while improving deployment reliability, scalability, security, and operational efficiency. The role partners with engineering teams to adopt DevOps practices, manage Kubernetes and cloud infrastructure, operate production systems, and automate environment provisioning, testing, and releases.

Develop and maintain Linux-based edge applications, containerized services, IoT data pipelines, and digital platforms. Deploy, monitor, and manage machine-learning models using MLOps tooling. Implement secure communications, device identity, software updates, testing, and lifecycle management. Collaborate with data science, platform, digital, and engineering teams to resolve issues and create data-driven services. Support onsite deployments, documentation, automation, and occasional international travel.

Leads a team of system administrators while remaining hands-on with cloud, container, and infrastructure operations. Responsibilities include designing resilient and secure services, supervising daily work, supporting GRC compliance, coordinating infrastructure projects, managing incidents, documenting systems, and participating in on-call rotation. The role requires substantial infrastructure or DevOps experience, team leadership, cloud and Kubernetes expertise, database knowledge, scripting skills, and familiarity with modern DevOps practices.

Develop and operate large-scale identity protection and anti-fraud systems defending Microsoft infrastructure against signup, telecom, phishing, spam, and revenue-share attacks. Responsibilities include designing, coding, testing, debugging, deploying, and supporting resilient features; applying machine learning and threat intelligence; analyzing metrics; ensuring reliability; collaborating cross-functionally; and participating in on-call rotation.

Design, deploy, and operate highly available database infrastructure across multi-cloud, virtualized, and Kubernetes environments. Migrate database services from virtual machines to Kubernetes, support disaster recovery and backup strategies, tune performance, and implement observability, logging, and alerting. Collaborate with engineering teams to resolve infrastructure challenges and automate workflows using scripting and infrastructure orchestration tools.

Build and deploy production-grade agentic AI solutions for enterprise customers. Design prompts, reasoning flows, tool calls, integrations, data pipelines, dashboards, and KPI reporting using Salesforce and customer platforms. Own technical components from architecture through production validation, collaborate directly with customers and partners, deliver proofs of concept and MVPs, and provide product feedback. The role requires customer-facing technical delivery, strong software engineering, LLM and data expertise, and approximately 25% travel.

Leads and mentors a software engineering team building secure, scalable, multi-tenant SaaS products on AWS. Responsibilities include architecture, technical roadmaps, delivery management, people leadership, production reliability, security and compliance, CI/CD, testing, and AI-assisted development. The role partners closely with Product and remains hands-on with Python development while driving engineering excellence and operational improvements.

Analyze complex datasets to identify trends, business drivers, and opportunities. Translate findings into actionable recommendations, dashboards, and reports using SQL and Power BI. Ensure data accuracy, monitor metrics, investigate quality issues, and streamline reporting workflows through automation and emerging technologies. Partner across business and technical teams while explaining insights to non-technical audiences and supporting data-led decision-making.

Designs, develops, deploys, and operates reliable, scalable cloud self-service platforms. Responsibilities include Kubernetes and cloud infrastructure engineering, observability, incident response, root cause analysis, performance optimization, CI/CD improvements, and operational automation. The role collaborates with internal teams to deliver secure, compliant, high-availability services supporting eBay products.

Early-career software engineer responsible for resolving production issues, investigating support cases, building automation and AI agents, and improving system reliability. The role involves writing, testing, reviewing, and deploying software; participating in incident response and support rotations; analyzing logs and root causes; and collaborating with mentors and product teams. Success is measured through reduced manual effort, fewer recurring issues, faster resolution, and improved reliability, with opportunities to advance into product or platform engineering.

Lead the design and development of AI solutions using machine learning, deep learning, NLP, and large language models. Research emerging AI technologies, build prototypes and products, develop solution architectures and pipelines, and identify opportunities for AI adoption. Collaborate across global teams, publish research, file patents, communicate technical direction, mentor junior data scientists, and lead project delivery.

Leads applied AI and data science research within Mastercard Foundry, designing and developing machine learning, deep learning, NLP, and generative AI solutions. Builds prototypes, products, architectures, and pipelines; researches emerging frameworks; works with LLMs; contributes to patents and research publications; and identifies AI opportunities. The role also mentors junior scientists, collaborates globally on innovation initiatives, and may lead projects and small teams.

Lead the research, design, and development of high-performance AI solutions using machine learning, deep learning, NLP, and language models. Build prototypes, products, architectures, and pipelines; conduct applied research; publish papers and file patents; and identify AI opportunities. Collaborate across global teams, mentor junior data scientists, communicate technical direction, and lead projects through solution delivery while applying strong software engineering and cloud practices.

Lead Data Scientist developing high-performance AI solutions using machine learning, deep learning, NLP, and large language models. Responsibilities include researching emerging AI technologies, creating prototypes and production capabilities, designing architectures and pipelines, collaborating across global teams, publishing research, filing patents, mentoring junior scientists, and leading solution delivery. The role requires Python, cloud, SQL, MLOps, API development, and strong communication and innovation skills.

Leads applied AI and data science initiatives, designing and developing machine learning, deep learning, NLP, and LLM solutions. Builds prototypes, products, architectures, and pipelines; researches emerging AI technologies; mentors junior data scientists; collaborates globally on innovation and R&D; communicates technical direction; and contributes to research papers and patents.

Leads applied AI and data science research to develop high-performance solutions using machine learning, deep learning, NLP, and large language models. Designs architectures, prototypes, APIs, and production capabilities; researches emerging AI technologies; mentors junior scientists; collaborates globally on innovation initiatives; and contributes to research publications, patents, technical communications, and project delivery.

Leads applied data science and AI research to design, develop, and prototype high-performance solutions using machine learning, deep learning, NLP, and large language models. Responsibilities include researching emerging AI technologies, building solution architectures and pipelines, developing APIs and interfaces, optimizing SQL, collaborating across global teams, publishing research, filing patents, identifying AI opportunities, mentoring junior scientists, and leading projects or small teams.

Lead Data Scientist developing high-performance AI solutions using machine learning, deep learning, NLP, and large language models. Responsibilities include researching emerging frameworks, creating prototypes and products, designing AI architectures and pipelines, developing APIs, collaborating across global teams, publishing research, filing patents, mentoring data scientists, and leading project delivery. The role requires strong Python, SQL, cloud, software engineering, and MLOps capabilities.

Lead applied AI and data science research within Mastercard Foundry, developing high-performance solutions using machine learning, deep learning, NLP, and large language models. Responsibilities include designing architectures and prototypes, researching emerging AI technologies, building APIs and pipelines, collaborating globally, mentoring junior scientists, presenting technical direction, and contributing to research papers and patents.

Lead Data Scientist developing high-performance AI solutions using machine learning, deep learning, NLP, and large language models. Responsibilities include researching emerging AI technologies, building prototypes and products, designing solution architectures and pipelines, optimizing APIs and SQL, collaborating across global teams, publishing research, filing patents, mentoring junior scientists, and leading project delivery. The role also involves identifying AI opportunities and applying cloud, MLOps, and software engineering practices.