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Senior AI Solutions Engineer – SalesInsurtech | Generative AI | Enterprise InsuranceAbout Our ClientOur client is a Series B–backed AI company transforming the property and casualty insurance industry through applied generative AI and intelligent automation. They work directly with large insurers to modernise core workflows, reduce operational cost, and improve customer experience using production-grade AI agents deployed across claims, policy, and service operations.They are scaling rapidly and are now hiring a senior, customer-facing AI Solutions Engineer to sit at the intersection of sales, product, and deployment.The RoleThis is a high-impact, technical commercial role. You will act as the technical authority throughout the customer lifecycle, from pre-sales through implementation and expansion.You will work closely with sales, customer success, and engineering teams to translate insurer requirements into robust, secure, and scalable AI solutions that deliver measurable business outcomes.This role suits someone comfortable owning conversations with CIO, CTO, and enterprise IT stakeholders while remaining hands-on with architecture, integrations, and demos.What You’ll DoPre-Sales & Technical DiscoveryPartner with sales teams to scope insurer requirements and design AI-driven solutionsPresent system architecture, security posture, and integration approaches to senior technical stakeholdersBuild tailored demos, proofs of concept, and solution prototypes aligned to insurer environmentsSolution Design & ImplementationAssess insurer core platforms including policy administration, claims, and billing systemsDesign and lead integrations across APIs, SSO, and workflow orchestrationSupport and guide deployments to ensure smooth onboarding and production readinessCustomer Advisory & ExpansionAct as a trusted technical advisor for insurer clients adopting AI at scaleIdentify opportunities for expansion across advanced AI agents, workflows, and APIsFeed customer insights back into product and engineering teams to influence roadmap prioritiesThought LeadershipRepresent the business in client workshops, technical sessions, and industry eventsProduce technical documentation, integration guides, and solution playbooks for enterprise customersWhat We’re Looking ForMust Have7–10 years in solutions engineering, pre-sales architecture, or enterprise technical consultingDeep experience within insurance technology ecosystems such as Guidewire, Duck Creek, Majesco, One Inc., Insuresoft, or SapiensStrong hands-on experience with APIs, cloud platforms (AWS, Azure, GCP), and enterprise integrationsProven ability to lead complex, multi-stakeholder enterprise implementationsExcellent communication skills with the ability to explain complex technical concepts to non-technical executivesComfortable operating in a fast-moving, high-ownership startup environmentNice to HaveExposure to AI, ML, automation platforms, or NLP-driven systemsBackground working in high-growth startups or insurtech vendorsFamiliarity with insurance compliance, data security, and regulatory frameworks

Product Developer – GeoAI & Spatial IntelligenceLocation: Europe (hybrid or remote options available)Level: Mid to SeniorAbout our client:Our client is a Swiss-Italian deep-tech company focused on applying machine learning to large-scale spatial data. By combining satellite imagery, mobility data, and crowd-sourced mapping, they deliver actionable intelligence across sectors including urban management, transportation, infrastructure, real estate, insurance, marketing technology, and socio-demographic analysis.They work at the intersection of Earth Observation, AI, and real-world decision-making, helping public and private organisations turn complex geospatial data into practical outcomes.The role:Our client is looking for a Product Developer to help shape the next generation of GeoAI-powered products. This role sits at the boundary between research, engineering, and the market.You will analyse past and ongoing projects, identify where research outputs can be transformed into scalable, market-ready products, and help define the product strategy moving forward. You will also assess market needs across multiple sectors and guide the innovation roadmap accordingly.This is a hands-on, cross-functional role requiring both strategic thinking and practical execution.Key responsibilities:Work closely with data scientists, engineers, researchers, and commercial teams to design and evolve GeoAI products.Translate complex AI, Earth Observation, and urban analytics outputs into clear, user-focused products.Conduct market analysis to identify unmet needs, sector gaps, and high-impact opportunities.Evaluate whether existing capabilities can solve identified problems, and define enhancements where needed.Define new products when market needs are not addressed by current solutions, ensuring feasibility and strategic alignment.Build and maintain a clear product roadmap prioritised by impact, value, and user needs.Gather and interpret feedback from users, clients, and market research to drive continuous improvement.Track product performance post-launch and recommend refinements or new initiatives.Required background:Bachelor’s or Master’s degree in Engineering, Data Science, GIS, Environmental Science, Business, Marketing, Product Management, or a related discipline.5 years of experience in product strategy or product development involving geospatial data.Strong problem-solving ability, with a track record of turning research, technical concepts, and market insight into concrete product plans.Solid understanding of software development lifecycles, data visualisation, and UX/UI principles.Comfortable working in fast-moving, multidisciplinary teams and with external stakeholders.Familiarity with remote sensing or urban analytics is required.Experience with AI, cloud platforms, geospatial tooling, or mapping libraries is a strong advantage.Why this opportunity:Join a fast-growing international deep-tech company operating at the forefront of geospatial AI.Work on real-world problems with tangible impact on cities, infrastructure, and society.Collaborate with leading public and private institutions on globally relevant projects.Flexible working environment with a strong emphasis on learning, creativity, and innovation.

AI Solution Architect – GenAI & Azure AI (Contract, Remote) We’re looking for a senior AI Solution Architect to lead the design of generative AI solutions built on Microsoft’s cloud and AI platforms. This role focuses on shaping end-to-end architectures for GenAI use cases and guiding delivery teams through to production. The role: You’ll own solution architecture across multiple generative AI initiatives, working from early use-case definition through to implementation. The focus is on designing scalable, secure, and production-ready AI solutions using Azure and Microsoft AI services. What you’ll be doingDesigning end-to-end architectures for generative AI and AI-driven applicationsTranslating business requirements into Azure-based solution designs and delivery approachesDefining patterns for LLM-enabled solutions, including search-augmented and retrieval-based architecturesMaking architectural decisions around Azure services, integration patterns, and deployment modelsProviding technical leadership to AI and engineering teams during deliveryReviewing solution designs and implementations to ensure quality, performance, and securityTechnical environmentMicrosoft Azure cloud services and PaaS componentsAzure AI and generative AI platforms (including LLM-based services and search-driven AI)Cloud-native architectures (serverless, containers, managed services)CI/CD pipelines and DevOps practices within Microsoft ecosystemsPython and/or modern Microsoft application stacksContract detailsInitial 3-month contract, guaranteed extension to 6 months (with strong potential to extend further)Fully remote€400–€425 per day

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.