Top Python Jobs in Dublin

Lead the architecture, development, and production deployment of AI-powered products, intelligent agents, and cloud-native analytics platforms. Build agentic workflows using LLMs, retrieval systems, orchestration, memory, evaluation, and safety controls. Develop scalable services and APIs with AWS, Azure, Databricks, Spark, Python, and Java. Drive engineering standards, CI/CD, security, observability, technical execution, architecture reviews, and mentorship across globally distributed teams.

Build and lead scalable full-stack, data-intensive applications and AI-powered products using distributed systems, cloud technologies, Databricks, Spark, and LLMs. Responsibilities include system design, API and service development, performance optimization, mentoring engineers, technical troubleshooting, secure coding, testing, observability, CI/CD, and cross-functional collaboration. The role guides team-level architecture and delivers reliable analytics-driven solutions for global customers.

Build and lead full-stack, data-intensive, analytics-driven applications for business experimentation and decision-making. Responsibilities include system design, distributed systems development, Databricks and Spark data processing, AI and LLM-powered products, performance optimization, cloud-native deployment, troubleshooting, testing, security, observability, CI/CD, and mentoring engineers.

Leads the design, development, and productionization of AI-powered products, intelligent agents, and agentic workflows. Architects scalable cloud-native platforms using LLMs, retrieval systems, orchestration frameworks, and modern data technologies. Provides hands-on software engineering, drives architecture and code reviews, coordinates global technical execution, improves reliability and developer productivity through automation and DevSecOps, and mentors engineers while ensuring security, observability, governance, and performance.

Lead the design, development, and production deployment of AI-powered products, intelligent agents, and cloud-native analytics platforms. Responsibilities include architecting agentic AI systems, building scalable services and APIs, establishing reliability and security practices, leading technical execution across global teams, conducting design and code reviews, driving CI/CD and DevSecOps, mentoring engineers, and improving platform scalability, observability, and developer productivity.

Lead the design, development, and production deployment of AI-powered products, intelligent agents, and agentic workflows. Architect scalable cloud-native systems using LLMs, retrieval, orchestration, and modern data technologies. Drive architecture reviews, engineering excellence, security, observability, CI/CD, testing, and platform modernization while coordinating distributed teams and mentoring engineers.

Build and lead scalable full-stack, data-intensive, analytics-driven applications for Mastercard Services. Responsibilities include system design, distributed systems, APIs, Databricks and Spark development, AI-powered products and agentic workflows, performance optimization, cloud deployment, reliability improvements, technical mentoring, code reviews, testing, security, observability, and cross-functional solution delivery.

Lead technical design and hands-on development of AI-powered products, intelligent agents, agentic workflows, scalable services, APIs, and analytics platforms. Drive architecture, productionization, security, observability, reliability, CI/CD, DevSecOps, and engineering excellence across globally distributed teams. Mentor engineers, lead technical decisions and reviews, and build reusable platforms using cloud, data, and modern AI technologies.

Designs and develops scalable full-stack, data-intensive, analytics-driven applications and AI-powered products. Responsibilities include system architecture, distributed systems, APIs, Databricks and Spark development, LLM-based agentic workflows, performance optimization, cloud-native deployment, testing, observability, troubleshooting, and mentoring engineers.

Designs and develops scalable full-stack, data-intensive, analytics-driven applications and AI-powered products. Leads system design, builds distributed services and data pipelines with Databricks and Spark, develops LLM-based agentic workflows, resolves performance and reliability issues, and mentors engineers. Collaborates cross-functionally to deliver secure, maintainable cloud-native solutions using modern software engineering, testing, observability, and CI/CD practices.

Lead hands-on development of scalable, cloud-native AI products, intelligent agents, and agentic workflows. Architect production AI systems with orchestration, reasoning, memory, grounding, evaluation, safety, observability, and governance. Build distributed services, APIs, analytics platforms, and developer productivity tools using AWS, Azure, Databricks, Spark, Python, and Java. Drive technical execution, architecture reviews, CI/CD, DevSecOps, engineering standards, cross-team alignment, and mentorship.

Designs and develops scalable full-stack, distributed, data-intensive, and AI-powered applications. Leads system design, builds analytics and agentic workflows using Databricks, Spark, and LLM technologies, resolves performance and reliability issues, and translates business needs into secure technical solutions. The role also involves mentoring engineers, conducting design and code reviews, improving CI/CD and observability, supporting cloud-native production systems, and collaborating across teams.

Lead the design, development, and production deployment of AI-powered products, intelligent agents, and cloud-native analytics platforms. Architect agentic systems with reasoning, memory, retrieval, evaluation, safety, and human-in-the-loop controls. Build scalable services, APIs, and data applications using AWS, Azure, Databricks, Spark, Python, and Java. Drive architecture reviews, engineering excellence, CI/CD, DevSecOps, technical execution, modernization, and mentorship across globally distributed teams.

Lead the architecture and development of scalable, cloud-native AI products, intelligent agents, agentic workflows, and analytics platforms. Design production-grade systems using LLMs, retrieval, orchestration, memory, evaluation, and safety controls. Build APIs and distributed services with Python, Java, AWS, Azure, Databricks, and Spark. Drive technical execution, architecture reviews, DevSecOps, automation, reliability, governance, and mentoring across globally distributed engineering teams.

Build and lead scalable full-stack, data-intensive applications and AI-powered products. Responsibilities include system design, distributed services, APIs, Databricks and Spark development, LLM agentic workflows, performance optimization, cloud deployment, troubleshooting, testing, observability, secure coding, and mentoring engineers.

Build and lead scalable full-stack, data-intensive applications and AI-powered products. Responsibilities include system design, distributed systems, microservices, APIs, Databricks and Spark development, LLM-based agentic workflows, performance optimization, cloud deployment, testing, observability, troubleshooting, and mentoring engineers. The role translates business needs into secure, maintainable technical solutions and drives engineering quality, reliability, and production readiness.

Build and lead scalable full-stack, data-intensive applications using distributed systems, cloud technologies, Databricks, Spark, and AI agentic workflows. Responsibilities include system design, API and data architecture, performance optimization, troubleshooting, secure coding, testing, observability, CI/CD, production readiness, cross-functional collaboration, and mentoring engineers.

Designs and develops scalable full-stack, distributed, data-intensive applications and AI-powered products. Leads system design, API and data architecture, performance optimization, cloud deployments, testing, observability, and production support. Collaborates with cross-functional teams to translate business needs into secure technical solutions, troubleshoots complex platform issues, and mentors junior and mid-level engineers. The role includes developing Databricks and Spark applications, LLM-based agents, retrieval systems, and agentic workflows.

Analyze anti-bot and bot-mitigation systems, identify how websites detect and block scraping activity, and develop repeatable testing and bypass approaches. Build a greenfield agentic environment for evaluating anti-bot behavior, contribute to backend systems at scale, investigate complex incidents, and communicate findings clearly. The role requires deep web protocol knowledge, strong Node.js, TypeScript, and Python skills, scientific problem-solving, AI coding agent proficiency, and independent execution in a remote-first team.

Design, build, and maintain scalable ETL/ELT pipelines and data integrations to consolidate, transform, and prepare large datasets for analytics and ML. Enforce best practices, data governance, monitoring, and support while collaborating with business and analytics teams to improve data quality and enable insight-driven decisions.

Test web applications across frontends and APIs, validate AI-driven and asynchronous outputs, identify and document defects, maintain test documentation, and advocate for quality throughout the development lifecycle. The role involves collaborating with product, development, and customer support teams, using SQL and CLI tools, and contributing to test automation and software architecture discussions.

Senior Software Engineer to design and implement C++/Python software, accelerate workloads with CUDA, develop and maintain Linux kernel modules and system services, optimize system and GPU performance, manage Linux systems, mentor junior engineers, and integrate AI technologies across products.

Own the architecture enabling AI-first product engineering, including agent-native standards, context infrastructure, self-healing incident workflows, autonomous agent features, and AI code review optimization. Ship full-stack product features, set technical direction, and partner with product and design teams. The role requires strong backend engineering, production LLM experience, distributed systems expertise, and practical use of AI coding tools.

Lead the architecture and implementation of AI-native product engineering systems, including agent-ready code standards, context infrastructure, self-healing incident workflows, multi-step agentic features, and AI code-review optimization. Ship end-to-end product features, set technical direction, evaluate LLM reliability and trade-offs, and accelerate validated learning through close collaboration with engineering, product, and design teams.

Lead architecture and end-to-end development of AI-first product features. Establish agent-native engineering standards, context infrastructure, self-healing incident workflows, multi-step agent systems, and AI code-review optimization. Collaborate with engineers, product managers, and designers to ship validated product improvements quickly while setting technical direction. The role requires strong backend and full-stack development, distributed systems expertise, production LLM experience, and effective use of AI coding tools.