Top Python Jobs in Dublin

Lead delivery of scoped software features and models by designing, developing, testing, debugging, and maintaining scalable solutions. Write reliable code, integrate AI/ML components, participate in design and code reviews, monitor application performance, support CI/CD, translate business needs into technical requirements, and mentor peers. Collaborate across engineering teams to deliver secure, maintainable, product-focused systems and contribute to continuous improvement and operational excellence.

Administer and improve EDR and SIEM platforms, tuning detection rules, correlation logic, endpoint policies, alerts, dashboards, and integrations. Maintain log collection, agent health, platform availability, and security visibility across endpoints, servers, cloud, and network systems. Support investigations, onboard new systems, convert security findings into monitoring improvements, track detection coverage, and produce documentation and runbooks.

Conduct independent AI research in digital pathology by building benchmark datasets and evaluation tasks, statistically characterizing control data, curating whole-slide images and metadata, validating pathology models, and collaborating with pathologists. The role involves reproducible analysis, scientific literature review, publishing, presenting results, and contributing to EU-funded research partnerships focused on virtual control groups and improved animal research methods.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

10-week onsite FPGA engineering internship focused on ultra-low-latency trading systems. Interns work across FPGA/ASIC design lifecycle: architecture, RTL implementation, simulation, verification, and hardware bring-up, guided by senior mentors on real projects.

Own and improve production infrastructure reliability, deployments, Infrastructure-as-Code, Kubernetes environments, automation, CI/CD, monitoring, alerting, and observability. Investigate incidents, optimize system performance, maintain documentation and runbooks, and support DNS, WAF, CDN, and caching infrastructure. The role requires strong Linux administration, Bash scripting, networking, Git, and containerization skills, with independent ownership and collaboration across development and operations teams.

Operate, support, and optimize production AI applications: troubleshoot pipelines and deployments, implement monitoring/observability, automate CI/CD and infrastructure (IaC), manage containerized workloads, perform RCA and capacity planning, and collaborate with AI, data, security, and product teams to ensure reliable, scalable AI operations.

Leads threat modeling, security reviews, vulnerability management, cloud and Kubernetes security, IAM protection, policy-as-code automation, and detection and response operations. Partners with engineering and DevOps teams to secure products, CI/CD systems, software supply chains, and production infrastructure. Develops scalable security tooling, establishes secure cloud baselines, prioritizes remediation, investigates incidents, and translates architectural findings into actionable controls.

Design, build, and ship full-stack features for a data-intensive orchestration platform. Implement APIs and integrations with CRMs and third-party systems, collaborate with data teams for signal processing, drive architecture and technical decisions, and help shape engineering practices as an early team member.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

The DevOps Engineer designs and manages AWS cloud infrastructure, automates operations, develops CI/CD pipelines, and ensures robust monitoring and observability.

As a Staff Software Engineer, you will lead the development of Apache Iceberg, enhancing data handling capabilities for large-scale systems, collaborating with teams, and architecting solutions for modern data lakes.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

As an Engineering Manager, lead a data engineering team to enhance Yuno's data platform, ensuring reliable processing of payment events, guiding technical decisions and cultivating a strong team culture.

The SOC L2/L3 Engineer is responsible for building and operationalizing the SIEM, designing detection rules, triaging alerts, leading incident responses, and maintaining SOC metrics on a payment processing platform.

Lead offensive security engagements across web/API, cloud (AWS), identity, and human attack vectors. Plan red team operations, run pentests and a bug bounty program, develop custom tooling, run purple team cycles with SOC, deliver risk-ranked reports, and track remediation to revalidation.

The Anti-Bot Engineer will research and implement evasion techniques against sophisticated anti-bot systems, debug TLS fingerprinting, and analyze browser behavior to optimize scraping methods.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

You will build and maintain features across frontend and backend stacks, design analytics dashboards, optimize APIs, and ensure database performance while collaborating with various teams.

Own end-to-end product engineering projects in a full-stack role: build APIs and backend systems, implement React/TypeScript frontends, make product and technical trade-offs, work with cloud-native tooling, and contribute to the open-source SuperPlane repo.

The AI Engineering Intern will design, test, and deploy scalable AI systems, integrate models, and work on inference optimization, collaborating with data scientists on complex problems.

The Quantitative Researcher will apply economic and statistical models to analyze large-scale data and derive insights, working with data scientists and engineers.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.