Senior MLOps Engineer – GPU Infrastructure & Inference Our client is building AI-native systems at the intersection of machine learning, scientific computing, and materials innovation, applying large-scale ML to solve complex, real-world problems with global impact. They are seeking a Senior MLOps Engineer to own and operate a production-grade GPU platform supporting large-scale model training and low-latency inference for computational chemistry and LLM workloads serving thousands of users. This role holds end-to-end responsibility for the ML platform, spanning Kubernetes-based GPU orchestration, cloud infrastructure and Infrastructure-as-Code, ML pipelines, CI/CD, observability, reliability, and disaster recovery. You will design and operate hardened, multi-tenant ML systems on AWS, build and optimize high-performance inference stacks using vLLM and TensorRT-based runtimes, and drive measurable improvements in latency, throughput, and GPU utilization through batching, caching, quantization, and kernel-level optimizations. You will also establish SLO-driven operational standards, robust monitoring and alerting, on-call readiness, and repeatable release and rollback workflows. The position requires deep hands-on experience running GPU workloads on Kubernetes, including scheduling, autoscaling, multi-tenancy, and debugging GPU runtime issues, alongside strong Terraform and cloud-native fundamentals. You will work closely with research scientists and product teams to reliably productionize models, support distributed training and inference across multi-node GPU clusters, and ensure high-throughput data pipelines for large scientific datasets. Ideal candidates bring 5 years of experience in MLOps, platform, or infrastructure engineering, strong proficiency in Python and modern DevOps practices, and a proven track record of operating scalable, high-performance ML systems in production. Experience supporting scientific, computational chemistry, or other physics-based workloads is highly desirable, as is prior exposure to large-scale LLM serving, distributed training frameworks, and regulated production environments.

We’re hiring an AI Program Manager to take ownership of a central AI delivery function and ensure high-impact AI initiatives move from idea to production at pace. This role is focused on execution, coordination, and decision-making across a broad set of stakeholders, rather than hands-on technical delivery. The role: You’ll be accountable for running a multi-stream AI program, balancing delivery momentum with governance, risk control, and transparency. Acting as the connective tissue between business leaders and technical teams, you’ll help shape how AI work is assessed, prioritised, and delivered across the organisation. What you’ll doLead the planning and execution of a portfolio of AI initiatives, with full accountability for timelines, funding, risks, and outcomesBring together teams across product, data, AI/ML, engineering, and security to deliver against shared objectivesPut in place clear intake and decision frameworks to evaluate AI opportunities and focus effort where it delivers the most valueActively manage delivery constraints, interdependencies, and trade-offs across multiple workstreamsContinuously evolve delivery processes to improve throughput, predictability, and stakeholder confidenceWhat you bringExtensive experience leading large-scale programs in complex, matrixed organisationsA strong track record of managing ambiguity, competing priorities, and senior expectationsWorking knowledge of how AI and data products are developed, validated, and deployed into live environmentsExperience designing operating models, governance forums, and prioritisation mechanismsClear, confident communication style with the ability to influence at executive levelA practical, results-oriented mindset with a bias toward action over theoryAI program delivery experience is a must have

MLOps EngineerLocation: Barcelona (Hybrid) Contract: Fixed-term until June 2026 Salary: €55,000 base pro rata Bonuses: €3,000 sign-on €500/month retention bonus Relocation: €2,000 package available Eligibility: EU work authorisation required The opportunity We’re hiring an MLOps Engineer to join a fast-scaling European deep-tech company working at the forefront of AI model efficiency and deployment. This team is solving a very real problem: how to take large, cutting-edge language models and run them reliably, efficiently, and cost-effectively in production. Their technology is already live with major enterprise customers and is reshaping how AI systems are deployed at scale. This is a hands-on engineering role with real ownership. You’ll sit close to both research and production, helping turn advanced ML into systems that actually work in the real world. What you’ll be working onBuilding and operating end-to-end ML and LLM pipelines, from data ingestion and training through to deployment and monitoringDeploying production-grade AI systems for large enterprise customersDesigning robust automation using CI/CD, GitOps, Docker, and KubernetesMonitoring model performance, drift, latency, and cost, and improving reliability over timeWorking with distributed training and serving setups, including model and data parallelismCollaborating closely with ML researchers, product teams, and DevOps engineers to optimise performance and infrastructure usageManaging and scaling cloud infrastructure (primarily Azure, with some AWS exposure)Tech you’ll be exposed toPython for ML and backend systemsCloud platforms: Azure (AKS, ML services, CycleCloud, Managed Lustre), plus AWSContainerisation and orchestration: Docker, KubernetesAutomation and DevOps: CI/CD pipelines, GitOpsDistributed ML tooling: Ray, DeepSpeed, FSDP, Megatron-LMLarge language models such as GPT-style models, Llama, Mistral, and similarWhat they’re looking for3 years’ experience in MLOps, ML engineering, or LLM-focused rolesStrong experience running ML workloads in public cloud environmentsHands-on background with production ML pipelines and monitoringSolid understanding of distributed training, parallelism, and optimisationComfortable working across infrastructure, ML, and engineering teamsStrong English communication skills; Spanish is a plus but not requiredNice to haveExperience with mixture-of-experts modelsLLM observability, inference optimisation, or API managementExposure to hybrid or multi-cloud environmentsReal-time or streaming ML systemsWhy this role stands outWork on AI systems that are already in production with global customersTackle real infrastructure and scaling challenges, not toy problemsCompetitive salary plus meaningful bonusesHybrid setup in Spain with relocation supportJoin a well-funded, high-growth deep-tech environment with long-term impact

We’re looking for a Data Engineer to help build and scale the data foundations that power modern AI and generative AI solutions. This role is focused on designing resilient data pipelines that support advanced analytics, ML, and LLM-driven use cases across a range of data types. The role: You’ll work closely with AI, ML, and platform teams to shape how data is collected, processed, and made available for downstream intelligence. The focus is on robust engineering, clean data, and systems that can scale as AI use cases move into production. What you’ll be doing:Building and maintaining Python-based data pipelines that handle ingestion, transformation, and enrichment of both structured and unstructured dataApplying AI-assisted techniques to data preparation, including classification, extraction, and feature creation to support ML and LLM workflowsConnecting data pipelines into Azure-based platforms, including data lakes and cloud-native servicesEnsuring pipelines are reliable and performant through testing, monitoring, and continuous optimisationPartnering with data scientists, AI engineers, and platform teams to support end-to-end AI deliveryWhat we’re looking for:Solid hands-on experience as a data engineer, with Python as a core languageProven experience delivering data pipelines in production environments at scaleExposure to AI, ML, or generative AI use cases within data platformsPractical experience working with Azure Data Lake and related Azure data servicesA strong engineering mindset with attention to data quality, system reliability, and performanceComfortable operating in collaborative, cross-functional teams

ML Scientist in AI Explainability  Location: Boston Massachusetts Type: Full time Machine Learning Scientist, AI Explainability and Scientific Discovery We are working with a publicly listed deep tech company operating at the intersection of machine learning, material science, and next generation battery technology. The team is applying AI directly to scientific discovery, with real world impact across energy storage, transportation, robotics, and aerospace. This role sits within an advanced AI research group focused on Large Language Models, AI agents, and explainability in scientific problem solving. Your work will directly influence how new battery materials are discovered and validated using AI. The position can be fully remote. What you will work on You will lead research into machine learning methods for scientific discovery, with a strong focus on multimodal Large Language Models and agent based systems.You will study how LLMs reason, plan, and generate solutions when applied to core scientific and engineering questions, particularly in battery and material design.You will design and optimize training pipelines for large models, tackling challenges around data quality, architecture, scalability, and compute efficiency.You will integrate domain specific data sources such as scientific literature and internal research documents into model training and inference.Your research will be deployed into a production multi agent AI system used for real battery technology discovery.You will collaborate closely with researchers, engineers, and external academic labs, and contribute to publications and conference presentations. What we are looking for An MSc or PhD in Computer Science, Statistics, Computational Neuroscience, Cognitive Science, or a related field, or equivalent industry experience.Strong grounding in machine learning, deep learning, and Large Language Models, with hands on research experience.Solid Python skills and experience with frameworks such as PyTorch or TensorFlow.Experience working with causal graphs and explainability focused AI methods.A proven research track record, ideally including peer reviewed publications.The ability to explain complex technical ideas clearly to both technical and non technical stakeholders.Nice to have Exposure to AI applied to material science, chemistry, or battery systems.Familiarity with recent research methods in LLM optimization and reinforcement learning approaches such as GRPO. What is on offerA highly competitive salary and benefits package, including equity in a publicly listed company.The chance to work on AI for science problems with visible global impact.A collaborative research environment alongside experienced ML scientists, engineers, and domain experts.Strong support for professional development, publishing, and long term career growth.

Senior SLAM Engineer Location: Zurich Type: Full-time, On-site Company Overview Our client is an early-stage robotics company developing autonomy and intelligent assistance systems for large-scale mobile machinery. By combining learning-based automation with advanced remote operation, thier technology enables a single operator to safely supervise and control multiple machines in complex, real-world environments. The team brings deep academic and industrial expertise in large-scale robotics and perception, and is focused on transitioning state-of-the-art research into production systems deployed on real machines operating in demanding conditions. Role Overview This role sits at the intersection of perception, state estimation, and real-world deployment. You will contribute to the design, implementation, and deployment of advanced localization and mapping solutions for autonomous and semi-autonomous heavy machines. The systems you work on integrate multiple sensing modalities—spanning lidar, vision, inertial sensing, and satellite positioning—into a hardware-agnostic autonomy stack that can be adapted to a wide range of machine types and vintages. The role requires not only strong algorithmic expertise, but also a focus on production-quality software and system robustness. Key ResponsibilitiesDesign, prototype, and deploy real-time localization, mapping, state estimation, and calibration algorithms for large autonomous mobile platformsDevelop SLAM pipelines leveraging lidar, inertial, visual, and GNSS data sourcesOptimize system performance, robustness, and reliability under real-world operating conditionsDefine and maintain testing procedures, validation strategies, and performance metricsCollaborate closely with engineers across perception, controls, systems, and hardware to improve end-to-end autonomy performanceEnsure high-quality, maintainable implementations suitable for deployment on production systemsRequired QualificationsMaster’s or PhD in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or a related field3 years of hands-on experience developing and deploying localization and mapping systemsStrong experience implementing SLAM and state estimation algorithms using lidar-inertial-visual sensor fusionProficiency in C and Python, with a focus on production-grade software developmentExperience working in Linux-based development environmentsAbility to manage technical risk, re-prioritize work, and meet deadlines in a fast-paced engineering environmentStrong communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiencesNice to HaveExperience integrating RADAR and/or GPS/GNSS into localization or SLAM systemsFamiliarity with ROS2 and modern robotics middleware

Applied AI Engineer  I am working with a fast growing AI company building an enterprise grade AI workspace used by major financial institutions to produce and validate client ready work. The platform replaces complex manual workflows with automated AI systems that scale across global teams and has grown rapidly with backing from top tier investors. This role is for engineers who want to build and ship production systems. You will own core parts of the AI agent infrastructure, including multi agent systems, RAG pipelines, and evaluation frameworks. The work is hands on and production focused, covering backend services, AI infrastructure, and delivery at scale. What you will doBuild and deploy backend services and APIs, Python preferred using Django or FastAPIProductionise AI features including RAG, agent orchestration, and evalsCreate data pipelines for training, evaluation, and continuous improvementEnsure performance, reliability, and security across the stackWork closely with founders, engineers, and product teamsWhat we are looking forFive plus years of software engineering experienceProven experience deploying AI applications into productionStrong backend engineering skills and database fundamentalsExperience with cloud infrastructure, Docker, Kubernetes, and CI CDBackground workers, task queues, and Redis experienceFamiliarity with LLM evaluation, monitoring, and safetyDegree from a Russell Group university or equivalent top tier academic background, or alternatively extensive engineering expertise with clear, relevant production experienceThis is a demanding, in office environment with high ownership, shifting priorities, and strong technical standards. You will work directly with founders who have built and exited venture backed companies. If you are an Applied or Agentic AI Engineer looking for real ownership and the chance to build core systems from the ground up, this is worth a conversation.

We are looking for a GenAI Engineer to join a growing Consulting organisation focused on AI solutions for the varied industries. You will play a key role in developing and integrating enterprise-level AI systems, contributing to the next generation of intelligent tools used by their clients. What You’ll DoDesign, build, and deploy GenAI applications using OpenAI APIs and LLM frameworksDevelop and optimise RAG pipelines for production useCollaborate with cross-functional teams to integrate AI into existing SaaS productsWrite clean, efficient, and scalable code, primarily in PythonContribute to architecture and design discussions around AI deployment and automationEngage with clients and internal teams to ensure alignment on project goalsWhat We’re Looking ForProven background in software development within SaaS or enterprise environmentsStrong practical experience using OpenAI APIs in commercial or large-scale settingsSolid understanding of LLMs, prompt engineering, and model deploymentHands-on experience with RAG pipelines and data retrieval optimisationExcellent communication and stakeholder management skillsAble to work independently and within collaborative teamsNice to HaveFrench for collaboration with teams in LausanneGerman for client interactions in ZurichWhy JoinFully remote flexibility with the option to work near Zurich or LausanneStable, long-term AI projects within the financial sectorClear growth trajectory with opportunities to contribute to upcoming initiativesSupportive, collaborative environment with positive team sentiment