Top Python Jobs in Dublin

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector search. Build knowledge bases, APIs, and microservices; architect scalable cloud/data platforms (AWS, Cloudera, Databricks); implement LLM orchestration (LangChain, MCP); monitor and optimize production model performance; ensure data governance, security, and compliance; mentor engineers and contribute to technical design.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector-search knowledge bases. Architect secure, scalable cloud/data systems (AWS, Cloudera, Databricks), build APIs and microservices, implement LangChain-based apps, optimize production model performance, and mentor engineers while ensuring data governance and regulatory compliance.

Lead the strategy, architecture, delivery, and operation of AWS-native platform services, infrastructure as code, CI/CD, developer tooling, and enterprise integrations across Atlassian, ServiceNow, Workday, and other SaaS platforms. Design secure, scalable cloud foundations and event-driven integrations, improve developer experience and operational quality, establish governance and observability, and mentor a global team while partnering with technology, cybersecurity, and business stakeholders.

Build and maintain backend services, APIs, data schemas, and scalable full-stack features for a multi-tenant SaaS cash forecasting platform. Own PostgreSQL data integrity and query performance, testing with RSpec, Docker deployments, AWS infrastructure, automation, observability, and performance optimization. Collaborate with product, design, and engineering teams, participate in code reviews and technical design, and join the on-call rotation.

Build and operate RenaissanceRe’s internal AI platform, including reusable tools, cloud-native deployments, enterprise integrations, monitoring, governance, testing, and responsible-AI controls. Develop AI agents, RAG frameworks, tool-calling workflows, and scalable services using Python, AWS, Docker, and orchestration frameworks. Support security, privacy, compliance, performance, reliability, and cost optimization while partnering with product, data, platform, and engineering teams to accelerate AI adoption.

Delivers client-facing advisory projects for charities and not-for-profit organizations, covering financial modeling, strategic planning, governance, sustainability, data analysis, and project management. Produces reports, presentations, dashboards, and recommendations while improving internal workflows through automation, AI, process standardization, and quality assurance. Coordinates international stakeholders, manages project workstreams, supports client engagements, and may mentor junior colleagues.

Supports Indonesian and broader Asian survey operations by monitoring data quality, managing quality-control staff, correcting survey issues, analyzing collection progress, and developing data-quality tools. Coordinates field operations, training, stakeholder engagement, translations, and backcheck processes. Provides insights on coffee and cacao sustainability data to country teams and supply-chain stakeholders. The role is fully remote from Indonesia with travel to regional agricultural areas.

Develop and optimize software solutions for industrial additive manufacturing in an agile team. Responsibilities include designing algorithms, data structures, and software subsystems; debugging complex issues; benchmarking and improving performance, stability, efficiency, and scalability; and contributing to software quality and documentation. The role uses Vue.js, TypeScript, Python, C++, MongoDB, Docker, and automated development tools.

Lead complex data analyses and data science projects using big data technologies. Develop systems, applications, and visual dashboards; assess system usage; improve data quality and processes; and build tools that address security threats and improve network reliability. Communicate technical findings to nontechnical audiences and guide peers through analytical methods, technologies, and system usage.

Provide technical support to B2B customers by troubleshooting API integrations, diagnosing issues, handling escalations, and collaborating with engineering teams. Develop internal tools and automation using APIs, create knowledge-base documentation, and communicate product capabilities clearly. The role also involves identifying recurring customer problems, sharing technical feedback, and influencing product improvements.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

Own data engineering for a cloud and SaaS billing integrations framework. Build and maintain third-party billing and usage API integrations, ensure data correctness and reconciliation, normalize vendor data, and manage orchestration, scheduling, ingestion, and backfills. Use AI throughout the engineering workflow, collaborate with product and support teams, and improve the scalability and reliability of pipelines that support financial decision-making.

Build and own DoiT’s integrations framework and customer-facing experience for connecting third-party billing and usage APIs. Develop authentication, incremental synchronization, rate-limit handling, error recovery, schema-drift management, testing tools, self-service diagnostics, credential rotation, backfills, and API-first capabilities. Deliver full-stack features from proposal through maintenance using TypeScript, React, Go, cloud services, and AI-augmented development practices.

Build and maintain integrations with third-party billing and usage APIs, including authentication, synchronization, rate limiting, error handling, schema changes, and self-service diagnostics. Develop the integrations framework, testing tools, customer-facing configuration and health interfaces, and API-first capabilities. Own features end to end while using AI throughout development. Collaborate remotely across the full stack using TypeScript, React, Go or another backend language, SQL, and cloud services.

Staff Data Scientist responsible for owning ambiguous, high-impact product and business problems; designing experiments; analyzing product data; developing analytical tools and frameworks; partnering with product, engineering, design, and leadership; and mentoring data scientists. The role requires advanced statistical expertise, strong product judgment, production programming experience, and the ability to translate complex analyses into strategic recommendations.

Develop and optimize large-scale advertising ranking models for CTR and CVR prediction, calibration, user representations, and sequence modeling. Own machine learning systems end to end, including data pipelines, feature engineering, training, evaluation, deployment, and maintenance. Collaborate with platform, product, data science, and engineering teams to run experiments and improve advertiser outcomes, revenue, and user relevance. Apply deep learning and recommendation techniques within production latency, reliability, and cost constraints.

As a Full Stack Engineer, you will own the complete stack from UI to API, ensuring high-quality feature development and exceptional user experiences at a fast-paced startup.

Build, operate, and improve production services that deliver machine learning models within exocad’s AI Design product. Responsibilities include model serving APIs, containerized deployments, versioning, staged rollouts, rollback automation, infrastructure provisioning with Terraform and AWS, monitoring, observability, and performance tuning. The role also contributes to training and labeling infrastructure and requires collaboration across engineering, product, and global teams, including work across AWS global and China environments.

Build and deploy agentic AI solutions in enterprise customer environments. Responsibilities include designing prompts, reasoning systems, tool calls, integrations, data pipelines, dashboards, and full-stack production deployments. The role partners with customers, sales teams, architects, and deployment strategists to deliver proofs of concept, MVPs, and measurable business outcomes. Engineers also evaluate AI outputs, troubleshoot platform gaps, share enablement, and provide product feedback. Approximately 25% travel is required.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Lead design and deliver enterprise-grade AI solutions using LLMs, RAG, LangChain and vector search. Architect scalable, secure cloud/data systems (AWS, Cloudera, Databricks), build knowledge bases and APIs, optimize models in production, mentor engineers, and ensure data governance, security, and compliance.

Owns global accounting operations, month-end close, journal entries, accruals, reconciliations, financial statement analysis, audit-ready workpapers, and US GAAP reporting. The role also manages digital asset wallet and crypto subledger reconciliations, partners with Treasury on liquidity activities, and embeds AI-assisted workflows and automations into accounting processes.

Develops Power BI dashboards, gathers stakeholder requirements, analyzes business metrics, and delivers actionable insights. Leads BI projects, mentors junior team members, maintains documentation and process compliance, supports data automation, and collaborates with IT and business solutions teams on data initiatives. Requires advanced Excel and SQL skills, expert Power BI knowledge, large dataset experience, and strong stakeholder and team management capabilities.

Deploy and integrate autonomous vulnerability-management platforms into high-security, sovereign, and air-gapped environments. Read and modify customer and platform code, identify vulnerabilities, verify patches, build security tooling integrations, and navigate authentication, network, compliance, and cross-domain constraints. Act as the technical bridge between customers and core engineering and AI teams while advising customer engineering, SOC, and operational leaders on automated defense and vulnerability remediation.

Develop and maintain API-based integrations and cloud-native Azure services. Build workflow automation, identity lifecycle processes, data synchronization, CI/CD pipelines, and Terraform infrastructure. Troubleshoot APIs, Azure services, and data sources while documenting integrations and scripts. The role requires Python or C#, Azure integration expertise, secure authentication knowledge, relational database experience, and familiarity with deployment automation.