Top Python Jobs in Dublin

As a Staff Software Engineer on the Inference team, you will optimize infrastructure for AI systems, tackling distributed systems challenges and enhancing performance for large-scale services.

Build and maintain large-scale inference systems serving Claude globally. Responsibilities include intelligent request routing, compute fleet autoscaling, deployment pipelines, accelerator integration, inference optimization, observability, multi-region deployments, and support for new model architectures. The role focuses on maximizing compute efficiency while enabling high-performance machine learning research infrastructure across diverse cloud platforms and hardware.

Lead the technical direction of Quora’s recommendation ecosystem, including feed retrieval, ranking, re-ranking, optimization, data pipelines, feature engineering, model training, and production integration. Drive measurable improvements through experimentation, apply generative AI, mentor engineers, and partner across product, data science, and infrastructure teams.

Designs and supports enterprise integration platforms that enable AI agents, APIs, MCP endpoints, automations, workflows, and connections to business systems. Builds reusable integrations across MuleSoft, Azure, SAP, ServiceNow, UiPath, Databricks, Fabric, and Denodo. Establishes governance for authentication, authorization, monitoring, logging, and human approvals while defining API, integration, and agent-to-agent standards. Troubleshoots platforms and collaborates with architecture, security, cloud, data, and product teams.

Develop secure, performant, reliable software features for Guidewire’s platform using agile practices. Prototype solutions from high-level requirements, troubleshoot failures across environments, collaborate with cross-functional teams, and maintain strong code quality. The role requires professional Java experience, object-oriented design skills, onsite work in Dublin at least two days weekly, and a commitment to continuous learning and responsible use of AI and data-driven insights.

Own and scale infrastructure supporting real-time APIs and internal services. Lead cloud architecture, deployment automation, observability, datastore optimization, capacity planning, reliability, and performance initiatives. Improve uptime, latency, cost efficiency, and developer velocity while driving infrastructure projects from roadmap through rollout. Collaborate on backend and distributed systems, mentor engineers, and establish operational best practices.

Build and operate scalable AWS cloud infrastructure centered on Amazon EKS, networking, infrastructure-as-code, and self-hosted services. Automate shard deployments, implement Terraform guardrails and secure ingress, support multi-region growth, and meet reliability, disaster recovery, compliance, and security requirements. Partner cross-functionally, troubleshoot application and infrastructure issues, participate in on-call and incident response, and use AI tools to improve engineering workflows.

Design and deliver internal systems, Go services, APIs, data models, dashboards, visualizations, and LLM-powered workflow features. The role includes writing specifications, estimating work, code reviews, testing, documentation, security, performance, accessibility, and collaboration with nontechnical stakeholders. The engineer will handle sensitive people data, mentor colleagues, and improve team practices in a globally distributed environment.

Serve as the technical partner interface to design, build, and maintain integrations and reference implementations between Nebius and strategic partners. Architect enterprise-ready solutions for GPU-accelerated AI/ML workflows, enable partner teams with docs and demos, influence product roadmap, and provide technical leadership for complex joint deployments and troubleshooting.

Manage and optimize mobile programmatic advertising campaigns toward CPA, ROAS, and retention goals. Responsibilities include bid, budget, publisher, audience, creative, geographic, and pacing optimization; A/B testing; SQL-based campaign analysis; dashboard and reporting support; LLM-assisted diagnostics; and mobile attribution analysis. The role collaborates with customer success, product support, and supply teams while supporting client reporting and QBR preparation.

Leads sample, kit, laboratory, biomarker, and associated data operations across global clinical studies. Oversees planning, logistics, vendor activities, data quality, dashboards, database queries, transfers, and database lock support. Collaborates with clinical, data management, biostatistics, laboratory, and operational teams to ensure protocol compliance, regulatory alignment, and high-quality precision medicine execution.

Analyze complex structured and unstructured datasets, perform statistical analysis, develop forecasting models, and create Power BI dashboards and reports. The role maintains data quality, supports self-service analytics, communicates insights and recommendations to stakeholders, and provides analytical support across multidisciplinary projects.

Build and deploy production-grade agentic AI solutions for enterprise customers. Responsibilities include designing prompts, reasoning, tool calls, integrations, data pipelines, dashboards, and full-stack components using Salesforce and cloud data platforms. Partner with customers, sales teams, strategists, and engineers to deliver proofs of concept, MVPs, and measurable business outcomes. The role requires customer-facing technical delivery, production software experience, AI and data expertise, fluent English, French, and Arabic, and approximately 25% travel.

Build and deploy production-grade agentic AI solutions within enterprise customer environments. Responsibilities include designing prompts, reasoning, tool calls, integrations, data pipelines, performance dashboards, and full-stack deployments using Salesforce and customer data platforms. Partner with strategists, customers, sales teams, and architects to deliver proofs of concept, MVPs, and measurable business outcomes. The role also requires evaluating AI outputs, sharing enablement, identifying platform gaps, and traveling approximately 25% to customer sites.

Build and deploy computer vision solutions for enterprise manufacturing, logistics, and warehouse customers. Responsibilities include coding integrations, configuring edge video pipelines, leading customer deployments, architecting safety solutions, translating customer requirements into product capabilities, and developing reusable integration frameworks. The role requires close collaboration with technical teams, executives, Sales, Customer Success, and Engineering, plus periodic travel to industrial customer sites.

Leads Citi’s OTC and ETF derivatives operating model within Investor Services Operations. Owns operational excellence, strategic roadmap delivery, process and technology change, risk controls, client relationships, team development, budgeting, and business growth. Partners with Product, Technology, Operations, Legal, Compliance, and Risk on complex transformation programs, while providing subject matter expertise in OTC derivatives, ETFs, and post-trade services.

Leads the OTC and ETF Operational Change function, setting its strategic roadmap and overseeing complex transformation programs, process redesign, testing, governance, operational risk, technology investment, and client implementations. Manages teams of managers and analysts, develops talent, owns budgets and headcount, supports business development, and represents the organization with senior clients and industry bodies. Drives automation, AI-enabled delivery, operational excellence, and cross-functional change across post-trade services.

Own and harden Blockdaemon’s multi-cloud infrastructure across AWS, GCP, and Azure. Build infrastructure as code with Terraform and Ansible, operate production GKE and GitOps deployments, manage network security, egress controls, Cloudflare, Tailscale, observability, and secure CI/CD pipelines. Participate in on-call and incident response, define reliability practices, automate manual work, and partner with security and platform engineering teams.

Own and improve Isla’s analytical data infrastructure, including BigQuery data models, pipelines, quality frameworks, dashboards, and reporting. Analyze complex datasets to measure product adoption, clinical efficiency, and patient outcomes. Enable self-service insights, support data-driven decisions across teams and healthcare clients, and guide data architecture and continuous improvement while collaborating with Product, Engineering, Delivery, and Commercial teams.

Fine-tune and post-train open-weight LLMs using methods such as SFT, LoRA, QLoRA, DPO, and GRPO. Build training data and evaluations, diagnose model failures, improve personalization and multilingual behavior, accelerate reproducible training workflows, and deploy models to production. The role involves using real user behavior to guide iterations across a large-scale AI companionship platform, with collaboration across AI, product, web, DevOps, and leadership teams.

Administer and support large-scale cloud and on-premises monitoring deployments across multiple providers. Maintain performance, availability, capacity, permissions, upgrades, and audit compliance. Integrate applications and infrastructure with enterprise monitoring instrumentation, document operational processes, collaborate with technical teams, and mentor colleagues on observability practices. The role requires expertise in monitoring platforms, application performance monitoring, synthetics, end-user experience, infrastructure monitoring, distributed tracing, cloud services, scripting, SQL, and multiple operating systems.

Build and lead scalable full-stack, data-intensive applications for analytics, experimentation, and decision-making. Responsibilities include team-level system design, distributed systems and API development, Databricks and Spark data processing, AI-powered agentic products using LLMs, performance optimization, cloud-native deployment, troubleshooting, secure coding, observability, CI/CD, and mentoring engineers.

Lead the architecture, development, and productionization of AI-powered products, intelligent agents, and cloud-native analytics platforms. Design agentic systems with reasoning, memory, retrieval, evaluation, safety, and human oversight. Build scalable services and APIs using AWS, Azure, Databricks, Spark, Python, and Java. Drive technical execution, architecture reviews, DevSecOps, testing, reliability, governance, and cost optimization while mentoring engineers and coordinating globally distributed teams.

Leads architecture and hands-on development of enterprise AI products, intelligent agents, agentic workflows, analytics platforms, and cloud-native services. Designs secure, scalable systems using LLMs, retrieval, orchestration, memory, evaluation, and safety controls. Guides productionization through reliability, observability, governance, CI/CD, testing, and cost optimization. Partners across engineering, product, data, security, and operations teams; coordinates global technical execution, drives design and code reviews, establishes reusable platforms, and mentors engineers.

Lead the architecture, development, and production deployment of AI-powered products, intelligent agents, and agentic workflows. Build scalable cloud-native services, APIs, analytics platforms, and distributed systems using AWS, Azure, Databricks, Spark, Python, and Java. Establish reliability, security, governance, observability, and cost-optimization practices while coordinating global engineering teams. Drive technical decisions, reviews, CI/CD, automated testing, modernization, reusable platforms, and mentorship across the engineering organization.