Top Python Jobs in Dublin

Own end-to-end infrastructure delivery for strategic physical AI customers, from discovery and architecture through production rollout. Build cloud compute orchestration, platform services, onboarding environments, observability, security, and cost controls for simulation, training, inference, and batch workloads. Debug across infrastructure layers, productize recurring customer needs, and shape the physical AI platform roadmap. Partner with customers, Field CTO, Product, and Engineering while using AI coding tools to accelerate production engineering.

Senior security researcher responsible for large-scale data collection and analysis, operating and building honeypots, performing vulnerability research and reverse engineering, developing automated tooling and GenAI/LLM workflows, collaborating with intelligence teams, reproducing vulnerabilities, and publishing technical research.

Build and automate cloud cost optimization and governance across AWS and GCP. Analyze billing and usage, identify and implement savings, maintain cost attribution, create reporting and anomaly detection, and write Python, Terraform, and Lambda automation. Partner with Engineering, Finance, Product, and leadership on architecture tradeoffs, forecasting, budgeting, and cost accountability.

Provide expert, customer-facing technical support for MongoDB ecosystems (server, drivers, cloud, infrastructure). Diagnose and resolve architecture, performance, recovery, security, and scalability issues; build support tools; mentor new hires; liaise with product and engineering on customer needs.

Research, fine-tune, train, and deploy large language models for real-time commentary and interactive avatar applications. Build RAG systems, agentic workflows, multimodal solutions, and production-grade inference services with monitoring and tool integration. Collaborate with ML engineers, product developers, and domain experts to create reliable, observable AI systems.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

Lead technical design and delivery of Klaviyo's messaging pipeline, building scalable backend services (Python/Go), driving architecture, mentoring engineers, collaborating across product, compliance, and platform, and ensuring high-throughput, reliable message delivery at scale.

Design, build, and own highly scalable, highly available messaging infrastructure that processes hundreds of millions to billions of messages daily. Collaborate across teams on architecture, performance, monitoring, and reliability; mentor engineers; improve development and experimentation processes; participate in hiring and technical design.

Design, build, and own features and services for Klaviyo's large-scale messaging platform. Drive performance, scalability, monitoring, and reliability for a pipeline that delivers hundreds of millions to billions of messages. Collaborate across teams, mentor engineers, and participate in hiring and technical process improvements.

Lead the company’s Physical AI research and engineering function as a hands-on technical leader. Define research priorities, design and train Transformer-based models, establish rigorous offline and real-world evaluations, integrate systems with physical robots, and analyze failures across data, models, hardware, and environments. Build a focused team, guide technical architecture and experiments, translate customer needs into requirements, and deliver measurable improvements through deployed models, benchmarks, evaluation systems, or data products.

Owns the design and delivery of payment risk systems covering underwriting, exposure, reserves, payout controls, disputes, verification, and transaction monitoring. Leads projects from discovery through rollout and reliability monitoring while balancing fraud protection with organizer experience. Collaborates closely with Product, Payment Operations, and KYC teams, participates in on-call and incident response, mentors engineers, and helps automate manual risk decisions for a growing merchant-of-record platform.

Lead software security practices across the development lifecycle by building and tuning vulnerability detection tools, triaging findings, prioritizing risks, improving security processes, and partnering with development and security teams. The role includes automating security workflows, evaluating vulnerabilities and mitigations, tracking security metrics, and promoting best practices across product teams.

Lead software security lifecycle improvements by deploying and tuning vulnerability detection tools, triaging findings, prioritizing security risks, and standardizing security processes. Partner with development, release, and Cisco security teams to remediate vulnerabilities, measure security posture, and promote best practices. Automate security workflows using Go, Python, Bash, or Terraform, with opportunities to apply LLMs, fuzzing, DAST, Kubernetes, and cloud security expertise.

Design and develop RTL for low-latency FPGA and ASIC trading applications, owning hardware designs from specification through verification and production deployment. Improve build, simulation, and verification infrastructure, and coordinate live hardware deployments. The role requires FPGA or ASIC experience, strong SystemVerilog or VHDL and cocotb skills, familiarity with vendor toolchains, and knowledge of high-speed interfaces such as Ethernet, PCIe, and AXI.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Lead incident response for critical production systems, triage and coordinate multi-team mitigation, improve availability through monitoring, automation, and post-incident improvements, and act as senior technical escalation on-shift.

Lead the architecture, design, and development of a cloud-native experimentation platform. Build scalable microservices, APIs, and analytics pipelines (Databricks/Spark), drive technical vision, mentor engineers, collaborate across global teams, and ensure scalability, security, observability, and continuous delivery.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector-search knowledge bases. Build scalable, secure cloud-native services and APIs, optimize model performance in production, mentor engineers, and enforce data governance and compliance.

Lead engineering and deliver production-grade AI solutions using LLMs, RAG, and vector-search knowledge bases. Architect scalable cloud solutions (AWS, Cloudera, Databricks), build APIs and microservices, implement MCP-style orchestration, monitor model performance, ensure security and compliance, and mentor engineering teams.

Lead design and deliver enterprise-grade AI solutions using LLMs, RAG, LangChain and vector search. Build and maintain knowledge bases, architect scalable cloud solutions (AWS, Cloudera, Databricks), develop APIs/microservices, ensure security/compliance, monitor model performance, mentor engineers, and collaborate cross-functionally to translate business needs into production AI systems.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector search. Build knowledge bases, APIs, and scalable cloud-native systems (AWS, Cloudera, Databricks). Mentor engineers, ensure security/compliance, optimize model performance, and collaborate cross-functionally to productionize AI applications.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector search. Build knowledge bases, LLM-driven apps (LangChain), secure cloud/databricks integrations, APIs/microservices, monitor model performance, and mentor engineers while ensuring compliance and operational excellence.

Lead the design and delivery of enterprise-grade AI solutions using LLMs, RAG, and vector-search knowledge bases. Build scalable, secure cloud-native systems (AWS, Cloudera, Databricks), develop APIs and microservices, implement LLM frameworks (e.g., LangChain), optimize models in production, and collaborate across product, engineering, and data teams while ensuring governance, security, and compliance.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector-search knowledge bases. Build scalable cloud architectures (AWS, Cloudera, Databricks), APIs and microservices, integrate LLM frameworks (e.g., LangChain), monitor model performance, and ensure security/compliance. Mentor engineers, drive technical design, and collaborate with product and data teams to productionize AI capabilities.

Lead design and delivery of enterprise-grade AI solutions using LLMs, RAG, and vector search. Build knowledge bases, LLM-driven applications (LangChain), and scalable cloud architectures (AWS, Cloudera, Databricks). Implement secure microservices/APIs, monitor model performance in production, mentor engineers, and ensure compliance with data governance and security standards.