Top Python Jobs in Dublin

Build and scale Euler’s AI-native PRM platform across the front end and back end. Develop React and Next.js applications, Python and Node.js services, CRM and partner-tool integrations, AWS and Cloudflare microservices, REST APIs, and deployment workflows. Collaborate with engineering and AI teams, apply secure coding practices, maintain CI/CD pipelines, and ship scalable features while working US East Coast hours.

Develop custom customer integrations and provide Bubble.io guidance. Build and maintain Salesforce, HubSpot, and other CRM integrations; manipulate data with Python and Pandas; design AWS Lambda microservices; support client onboarding and collaborate with the development team to improve the platform.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Develop, validate, deploy, and maintain machine learning solutions for medical device manufacturing and CAD/CAM systems. Build deep learning and multimodal AI models using computer vision, 3D geometry, meshes, images, and structured data. Integrate models into production systems, conduct performance analysis, maintain ML workflows, and translate research into practical manufacturing applications while collaborating with engineering, manufacturing, and product teams.

Designs and delivers AI-native solutions across business functions including People, Legal, Finance, and Operations. Responsibilities include identifying transformation opportunities, building and deploying AI agents and automated workflows, integrating enterprise systems, driving adoption, measuring business impact, applying governance and human oversight, and mentoring colleagues. The role partners with functional leaders to redesign operating models and continuously improve production AI solutions.

Own and operate the AI security runtime control plane across Azure, AWS, and GCP. Build inline AI enforcement, tune prompt-injection, data protection, tool-call, and agent access policies, and integrate security platforms with SIEM/SOC workflows. Lead L3 investigations, automated containment, cloud and Kubernetes security, DevSecOps controls, vendor evaluations, and closed-loop red-team remediation. The role requires automation, infrastructure-as-code security, multi-cloud identity management, incident response, and collaboration with technical leadership.

Provides L2 monitoring, triage, investigation, and escalation for AI and cloud security detections. Analyzes telemetry using KQL or SQL, maintains dashboards, and produces operational, compliance, and risk reporting. Investigates AI threats including prompt injection, jailbreaks, data leakage, agent hijacking, and unauthorized tool activity. Monitors Azure, AWS, and GCP posture, supports vulnerability and compliance remediation, documents cases, and partners with SOC, cloud, incident response, and engineering teams.

Architects and delivers enterprise AI, machine learning, generative AI, RAG, and agentic systems. Designs secure end-to-end architectures, integrates Claude and other models with ServiceNow, APIs, databases, cloud platforms, and workflows, and leads discovery, proofs of concept, and client delivery. Develops ML and NLP solutions, data pipelines, dashboards, evaluation frameworks, observability, governance, and responsible AI controls. Collaborates with stakeholders, engineers, consultants, and clients to operationalize reliable AI applications.

Build and own automated testing frameworks, CI/CD infrastructure, load and stress testing, and chaos engineering practices for a brokerage platform. Write primarily Go, integrate AI and LLM tooling into quality workflows, investigate application and Kubernetes failures, improve developer tooling, and mentor engineers on reliability and test coverage.

Build and operate production AI products using generative AI, LLMs, RAG, vector search, and agentic systems. Develop scalable machine learning services, pipelines, APIs, infrastructure, and automated evaluation systems. Improve AI quality, reliability, security, performance, and cost while establishing engineering standards. Collaborate with Product, Engineering, Data Science, Data Engineering, and Analytics teams to deliver dependable customer experiences.

Own end-to-end security reviews for SaaS, vendor integrations, corporate tools, cloud migrations, and AI/LLM products. Assess identity, access, data flows, configurations, agentic architectures, and AI security risks; document actionable dispositions under SLAs. Build scalable triage, risk-tiering, review rubrics, automation, and guardrails for AI-assisted reviews. Partner with risk, privacy, legal, identity, cloud, and network security teams while maintaining frameworks-based assurance and cross-training reviewers.

Leads the technical direction of an application security team while remaining hands-on with vulnerability triage, detection engineering, cloud security, exploitability assessment, and security automation. The role oversees SAST, SCA, DAST, secrets scanning, mobile and cloud security coverage; investigates high-risk application and cloud issues; coordinates bug bounty and penetration-testing workflows; and develops AI-assisted processes for triage and false-positive reduction. Requires strong expertise in web, API, AWS, multi-tenant SaaS, software supply chain, and application security leadership.

Build and own Supabase’s internal AI execution platform, including event-driven agent runtimes, durable state, human review, rollback, observability, evaluations, governance, and least-privilege tooling. Develop production agents, MCP integrations, infrastructure on Python and GCP, CI evaluation gates, operating metrics, and return-on-investment instrumentation. As the sole platform engineer, independently make architecture decisions, operate systems end to end, and ensure unsafe actions are structurally impossible.

Plans and executes full-scope red team and adversary emulation operations, including phishing, C2, privilege escalation, lateral movement, EDR/XDR evasion, Active Directory and cloud compromise, and social-engineering scenarios. Develops custom offensive tooling, collaborates with Blue Teams to validate detections, documents attack chains, and provides remediation guidance. Requires 5+ years of offensive security experience, advanced scripting skills, and recognized offensive security certifications, with bug bounty or CTF achievements preferred.

Build a model enablement platform for heterogeneous AI computing across AIPUs, CPUs, GPUs, NPUs, and other accelerators. Responsibilities include model ingestion, graph partitioning, compilation, runtime execution, performance profiling, memory and scheduling optimization, large-model offloading, and verification integrated into CI. The role combines architecture ownership with hands-on C++ and Python engineering across model frontends, compilers, runtimes, and application pipelines.

Own LLM and VLM enablement on Axelera AI hardware, from studying and profiling models through productionizing them in the Voyager SDK. Responsibilities include PyTorch references, quantization, weight mapping, model contracts, pipeline integration, KV-cache management, multimodal serving, accuracy validation, and bring-up platform development. The role also shapes GenAI, agent, and speech capabilities for edge devices.

Own growth engineering for Viktor by identifying activation and retention drop-offs, designing and shipping experiments, improving onboarding and product discoverability, building referral and invitation loops, and instrumenting changes for measurement. The role requires end-to-end product engineering across TypeScript/React, Python, databases, and AI-enabled workflows, with direct user interaction and responsibility for measurable outcomes.

Lead research on how AI and datacenter growth reshapes power grids, utilities, and industrial infrastructure. Produce forecasts, reports, and briefings; track transmission, distribution, equipment supply chains, and capex cycles; collaborate with semiconductor and datacenter analysts; support institutional clients and present findings to leadership and at conferences.

Build and maintain ETL/ELT pipelines, cloud data warehouse models, dashboards, and reports. Ensure data quality, governance, lineage, and reliable metric definitions while translating stakeholder needs into actionable analytics. Support AI-enabled reporting, natural-language querying, anomaly detection, and governed LLM tools. Collaborate with business, IT, and engineering teams to automate reporting and improve self-service data access.

Own Coinflow’s defensive and offensive security posture by operating the SIEM, conducting internal penetration tests, managing vulnerabilities and dependencies, improving the secure development lifecycle, and supporting PCI DSS, SOC 2, ISO 27001, and DORA compliance. The role also includes security monitoring, incident triage, threat modeling, security tooling automation, and collaboration with engineering teams across cloud and application environments.

Lead the AI architecture for Productboard Insights, designing agent-native systems, context infrastructure, self-healing workflows, evaluation processes, and AI code-review optimization. Own technical direction across Insights, Spark, and platform teams; drive features from concept through beta; optimize quality, latency, cost, and reliability; write production code; and develop engineers through documentation and reviews.

The Data Analyst develops and maintains Power BI dashboards, datasets, data models, SQL and DAX solutions, and data pipelines. The role supports AWS cloud analytics integration, data quality, troubleshooting, governance, documentation, and workspace administration. It coordinates data engineers, translates business requirements into technical deliverables, collaborates with stakeholders, and communicates findings to nontechnical audiences while supporting agile delivery and data privacy compliance.

Develop and maintain scalable backend services for a cloud-native securities-based lending platform using Python, FastAPI, Java, and Spring Boot. Design REST APIs and microservices, deploy resilient AWS solutions, maintain CI/CD pipelines and automated tests, troubleshoot production issues, participate in architecture and Agile ceremonies, conduct code reviews, and mentor junior engineers.

Leads an enterprise data engineering team, overseeing people development, delivery planning, technical architecture, production reliability, and operational support. Guides scalable batch and streaming pipelines, ETL/ELT, data products, integrations, governance, testing, CI/CD, monitoring, and incident response. Partners with engineering, platform, security, architecture, and business stakeholders to deliver secure, reusable, high-quality data capabilities across enterprise domains.

Lead and grow a multidisciplinary computer vision engineering team while remaining hands-on with code and architecture. Set technical direction for production computer vision, deep learning, edge deployment, real-time video processing, and vision-language models. Own delivery from scoping through deployment, coordinate cross-functional dependencies, improve development and release workflows, and resolve production detection-quality issues. Hire, coach, establish engineering standards, and connect model quality to customer outcomes.