Top Python Jobs in Dublin

The AI Applied Scientist Intern will tackle complex problems using deep learning, statistical analysis, and large datasets, collaborating with a team to implement AI solutions that generate economic impact.

Design, maintain, and optimize HLR/HSS/UDM subscriber templates and roaming logic. Lead Full MVNO profile deployments, collaborate with roaming partners and system integrators, define HLR/HSS KPIs, and troubleshoot signalling using Wireshark and roaming platforms.

Develop and productionize avatar-centric video diffusion models: incorporate multimodal conditioning, enable real-time long video streaming, improve lip-sync and motion realism, build evaluation frameworks, collaborate with data teams, and translate research into product features.

Lead architecture and reliability for cloud infrastructure and Kubernetes, improve observability and IaC, drive resilience and incident response, mentor engineers, and shape the platform roadmap.

Hands-on senior data engineer to design and build scalable Lakehouse data solutions, develop ETL/ELT and streaming pipelines, tune Spark workloads, manage Delta Lake ingestion and governance, implement infrastructure with Terraform, enforce IAM and data security, monitor pipeline health, collaborate in Agile with data scientists and stakeholders, and explore AI tooling to enhance the platform.

Design and implement AI-powered internal tools for GTM use cases, managing full lifecycle from data modeling to production. Collaborate closely with GTM teams to translate data into actionable insights with a focus on measurable business impact.

Lead fund accounting conversions for alternatives: define requirements, map and migrate data, design reconciliations and controls, partner with Technology/Data for ETL, and ensure NAV, investor servicing, and reporting readiness post-conversion.

The Senior Data Analyst will support the Investment Management team, translating complex datasets into insights, developing analytical models, and collaborating with data engineers on analytics and data solutions.

Develop and maintain front-end and back-end components of AI-driven products, collaborating with teams to enhance user experience and build proof of concept products.

Lead verification of complex SoC and FPGA subsystems using constrained-random, formal and C-based methods. Develop reusable UVM testbenches, integrate AI/ML verification tools, collaborate with emulation/prototyping and architecture teams, and mentor junior engineers to ensure first-silicon success.

Design and maintain reusable UVM-based testbenches and verification environments for next-generation secure, low-power FPGAs. Perform constrained-random, formal and C-based verification, RTL and power-aware verification, collaborate with emulation, prototyping, architecture and design teams, and apply AI/ML-enabled verification techniques to improve coverage and productivity.

Lead design and delivery of reliability and platform engineering work: operate and scale Kubernetes clusters, build cloud infrastructure, implement IaC, define SLO/SLI/error budgets, improve observability, automate CI/CD, embed AI-driven workflows, mentor engineers, collaborate with Security, and participate in incident response and on-call rotations.

Lead performance analysis and optimization of large-scale GPU/HPC clusters across hardware and software stacks. Troubleshoot real workloads, qualify and integrate new hardware, tune system configurations (networking, virtualization, kernel), support escalations, and collaborate with infrastructure teams and hardware vendors to ensure cluster performance meets expectations.

Maintain and scale DevTools SRE systems: improve user-facing developer workflows, build fault-tolerant self-healing architecture, optimize performance, modify open- and closed-source tools (GitLab, TeamCity), and support users while measuring improvements via metrics.

Build post-training infrastructure for voice and text agents, including training environments, verifiers, synthetic data pipelines, evaluations, and end-to-end experiments. Select and adapt open-weight models, design robust rewards, deploy models to production, and improve behavior using real-world usage. The role requires strong Python engineering, experience taking models from raw data to production, GPU familiarity, and collaborative remote work.

Design and implement the core components of the Agents Python framework, focusing on API and abstraction development for AI-driven applications.

Build post-training infrastructure for voice and text agents, including training environments, verifiers, synthetic data pipelines, evaluations, and model release gates. Run end-to-end training experiments, select open-weight models, improve tool-using and multi-turn behavior, deploy models to production, and iterate using real-world usage. The role emphasizes Python engineering, reinforcement learning, GPU-based training, data quality, reward design, and production machine learning.

Design and develop functionalities for the Agents framework, iterate on feedback, and create documentation while working with various programming languages.

10-week onsite FPGA engineering internship focused on ultra-low-latency trading systems. Interns work across FPGA/ASIC design lifecycle: architecture, RTL implementation, simulation, verification, and hardware bring-up, guided by senior mentors on real projects.

Design, build, and ship full-stack features for a data-intensive orchestration platform. Implement APIs and integrations with CRMs and third-party systems, collaborate with data teams for signal processing, drive architecture and technical decisions, and help shape engineering practices as an early team member.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

Own and improve production infrastructure reliability, deployments, Infrastructure-as-Code, Kubernetes environments, automation, CI/CD, monitoring, alerting, and observability. Investigate incidents, optimize system performance, maintain documentation and runbooks, and support DNS, WAF, CDN, and caching infrastructure. The role requires strong Linux administration, Bash scripting, networking, Git, and containerization skills, with independent ownership and collaboration across development and operations teams.

Leads threat modeling, security reviews, vulnerability management, cloud and Kubernetes security, IAM protection, policy-as-code automation, and detection and response operations. Partners with engineering and DevOps teams to secure products, CI/CD systems, software supply chains, and production infrastructure. Develops scalable security tooling, establishes secure cloud baselines, prioritizes remediation, investigates incidents, and translates architectural findings into actionable controls.

The DevOps Engineer designs and manages AWS cloud infrastructure, automates operations, develops CI/CD pipelines, and ensures robust monitoring and observability.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.