ai platform engineer

Maastricht University

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 AI Platform Engineer
 
Welkom bij de Universiteit Maastricht!
 
Wil jij meebouwen aan een AI-platform dat innovatie in onderwijs, onderzoek en bedrijfsvoering mogelijk maakt? Binnen ICT Service Centre (ICTS) krijg je de kans om een sleutelrol te spelen in de ontwikkeling van een veilige, schaalbare en toekomstbestendige AI-omgeving voor een internationale universiteit. Hier werk je aan technologie die niet alleen vandaag impact maakt, maar ook de basis legt voor de universiteit van morgen.
 
AI Platform Engineer
 
Ons doel: Binnen de Universiteit Maastricht groeit het gebruik van artificiële intelligentie in hoog tempo. Het team Cloud Foundation (Cloud & AI) ontwikkelt en beheert het cloud- en AI-platform waarmee onderzoekers, docenten, ondersteunende diensten en studenten veilig, verantwoord en efficiënt gebruik kunnen maken van AI-technologie. Zo creëren we de technische basis voor innovatie en digitale transformatie binnen de hele universiteit.
 Jouw collega's: Je maakt deel uit van het team Cloud Foundation (Cloud & AI), een gespecialiseerd team binnen ICTS dat verantwoordelijk is voor het Azure-platform van de universiteit. Je werkt nauw samen met Cloud Engineers, DevOps Engineers, een Product Owner, Cloud Architect en AI Architect. Samen bouwen jullie aan een moderne cloud- en AI-omgeving die betrouwbaar, schaalbaar en toekomstbestendig is.
 
Als AI Platform Engineer zorg jij ervoor dat AI-oplossingen veilig, reproduceerbaar en beheersbaar kunnen worden ontwikkeld en ingezet. Je ontwikkelt zelf geen AI-modellen, maar creëert de technische randvoorwaarden waarmee onderzoekers, ontwikkelaars en andere gebruikers succesvol kunnen innoveren. Daarmee speel je een belangrijke rol in de verdere professionalisering van AI binnen de Universiteit Maastricht.
 
Wat jij doet
 
Je ontwerpt en implementeert AI Landing Zones binnen Microsoft Azure.
 Je integreert AI-diensten zoals Microsoft Foundry (Tools) en Azure Machine Learning.
 Je ontwikkelt gestandaardiseerde bouwblokken voor AI-toepassingen, zoals RAG-oplossingen en andere herbruikbare platformcomponenten.
 Je richt CI/CD-processen in voor AI-modellen, prompts en deployments en automatiseert deze waar mogelijk.
 Je implementeert monitoring, logging, versiebeheer en kwaliteitscontroles binnen AI-workloads.
 Je realiseert governance-, privacy- en securitymaatregelen die bijdragen aan verantwoord AI-gebruik en compliance met wet- en regelgeving.
 Je optimaliseert prestaties, schaalbaarheid en kosten van AI-oplossingen binnen Azure.
 Je ondersteunt interne teams met self-service oplossingen, referentiearchitecturen en best practices voor AI-ontwikkeling.
 
Ben jij klaar om koers te zetten voor de komende jaren? Dan ontmoeten we je graag.
 
Wat jij meebrengt
 
Het gaat ons niet om vinkjes, maar om wie jij bent en wat jij meebrengt. Herken jij jezelf hierin?
 
Cloud-native expertise: Je hebt ervaring met engineering binnen Microsoft Azure en begrijpt hoe moderne cloudplatformen worden ontworpen, ingericht en beheerd.
 Automatisering: Je hebt ervaring met Infrastructure as Code, bijvoorbeeld met Bicep of Terraform, en ziet automatisering als een vanzelfsprekend onderdeel van je werk.
 DevOps-mentaliteit: Je bent bekend met CI/CD-processen, Docker, Kubernetes en moderne software delivery-praktijken.
 Platformdenken: Je denkt in schaalbare oplossingen, standaardisatie en herbruikbaarheid en bouwt graag voorzieningen waarmee anderen succesvol kunnen werken.
 Samenwerken en kennisdelen: Je documenteert zorgvuldig, deelt actief kennis en ondersteunt collega's graag bij het realiseren van innovatieve oplossingen.
 
Ervaring met Azure Machine Learning, MLOps, LLMOps, observability-oplossingen, identity & access management of data-integraties zien we als een mooie aanvulling. Belangrijker vinden we jouw vermogen om nieuwe technologie snel eigen te maken en jouw enthousiasme om mee te bouwen aan het AI-platform van de toekomst.
 
Wat je van ons mag verwachten
 Bij de Universiteit Maastricht werk je in een internationale, open en betrokken omgeving. We bieden:
 
Een jaarcontract (0,8 - 1,0 fte) met uitzicht op een vast contract bij wederzijdse tevredenheid.
 Een bruto salaris tussen €3546,00 en €6433,00 bruto per maand (bij een fulltime dienstverband van 38 uur per week), exclusief 8% vakantiegeld en een 8,3% eindejaarsuitkering.
 29 vakantiedagen, vier extra vrije feestdagen en de mogelijkheid om 12 extra dagen op te bouwen via compensatie-uren.
 Flexibele werktijden, een thuiswerkvergoeding en hybride werken.
 Vrijheid en ruimte om je werk zelfstandig in te richten en je eigen ideeën te ontwikkelen.
 Een hechte community van collega’s waarmee je nauw samenwerkt.
 Een goede pensioenregeling via het ABP, bedrijfsfitness en toegang tot een uitgebreid sportaanbod.
 Een inspirerende werkomgeving in het hart van Europa.
 
Over ICT Service Centre (ICTS)
 Bij ICTS bouwen we aan slimme, veilige en toekomstbestendige IT-oplossingen voor onderwijs, onderzoek en bedrijfsvoering. Als betrouwbare serviceprovider leveren we stabiele en hoogwaardige IT-dienstverlening. Tegelijk zijn we een strategische partner binnen de universiteit: we denken actief mee over innovatie en digitale transformatie. In alles wat we doen, staat de mens centraal of het nu gaat om studenten, medewerkers of onze eigen collega's. Onze 140 professionals werken in zelforganiserende teams volgens de Agile-methode. Zo blijven we wendbaar, dienstverlenend en gericht op continue verbetering.
 
Nieuwsgierig geworden?
 
Wil je meer weten over deze functie of over werken bij onze universiteit? Neem contact op met Kevin Vancraybex, 06 28586481, kevin.vancraybex@maastrichtuniversity.nl.
 
Sollicitaties worden doorlopend beoordeeld en de vacature blijft openstaan totdat een geschikte kandidaat is gevonden. We raden je daarom aan om zo snel mogelijk te solliciteren.
 
Solliciteer via onderstaande knop. We hopen je snel te leren kennen!
 
Over de Universiteit Maastricht
 Aan de Universiteit Maastricht zoeken we samen naar antwoorden waarmee we de wereld vooruithelpen. Dat doen we samen met 23.300 studenten en 5.400 medewerkers, verspreid over 5 steden in de regio, 6 faculteiten en ruim 70 onderzoeksinstituten in de Euregio. Hier krijg je de ruimte om grenzen te verleggen en nieuwe kansen te ontdekken; voor jezelf en voor de wereld om je heen. Want echte antwoorden vinden we samen.
 
De vacature staat open voor interne- en externe kandidaten. Bij gelijke geschiktheid hebben interne kandidaten voorrang.
 
Bij Maastricht University nemen we graag zelf contact op met potentiële kandidaten. We vragen daarom vriendelijk om geen acquisitie of aanbiedingen van bureaus of tussenpersonen.
 
Universiteit Maastricht zet zich in voor het bevorderen en behouden van een diverse en inclusieve gemeenschap. Wij geloven dat diversiteit in onze medewerkers- en studentenpopulatie bijdraagt aan de kwaliteit van onderzoek en onderwijs aan de UM. Dit willen we mogelijk maken door middel van inclusief beleid en innovatieve projecten in samenwerking met studenten en medewerkers. We moedigen je aan om voor deze functie te solliciteren.
 
Soort vacature: Non-Academic
 Faculteit/Servicecenter: ICT Service Centrum
 Sluitingsdatum: 16-06-2027
 FTE minimaal: 0,8
 FTE maximaal: 1,0
 Salaris min: €3546,00
 Salaris max: €6433,00
 ID vacature: 3404
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 Soort vacature: Non-Academic
 Faculteit/Servicecenter: ICT Service Centrum
 Sluitingsdatum: 16-06-2027
 FTE minimaal: 0,8
 FTE maximaal: 1,0
 Salaris min: €3546,00
 Salaris max: €6433,00
 ID vacature: 3404
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AI Platform Engineer

COLLINSON

<div>Collinson is a global loyalty and benefits company.
We use our expertise and products to craft customer experiences which enable some of the world&rsquo;s best-known brands to acquire, engage and retain the most demanding and choice-rich customers. In particular, our unique expertise and insight into high earning, frequent travellers allow us to create products and solutions for our clients that inspire greater customer engagement to drive more profitable relationships, enrich their travel experiences, protect what matters and assist in in times of need.
While specializing in Financial Services, Travel and Retail, we also support clients in multiple sectors. We have worked with over 90 airlines, 20 hotel groups and more than 600 financial institutions and banks, with clients including Accor Hotels, Air France KLM, American Express, British Airways, Cathay Pacific, Diners Club, Mandarin Oriental, Mastercard, Radisson Hotel Group, Sephora, Visa and Vhi.
We take our 30 years&rsquo; experience working with these kinds of household names in over 170 countries and help our clients to deliver the smarter experiences it takes to differentiate their propositions and help them win deeper devotion with their customers.
Collinson is a privately-owned entrepreneurial business with 2,000 passionate people working in 20 locations worldwide. Our solutions include Priority Pass, the world&rsquo;s best known airport experiences programme, while we are also the trusted partner behind many of the leading financial services, airline and hotel brand&rsquo;s reward programmes and loyalty initiatives.
Purpose of the job
We're looking for an AI Platform Engineer, someone who believes the ability to work effectively with AI should belong to every team, not just a handful of specialists.
This role is pivotal to how Collinson adopts and scales AI across our engineering teams. You'll build the platform, tooling, and practices that let teams deploy AI capabilities safely and effectively, laying the foundations for how we work with AI as an organization, focusing on integrating AI within our Software Developer Lifecycle (SDLC).
We're early in our AI journey, so you'll operate with a degree of autonomy and ambiguity, working consultatively with teams to understand what they need and helping them get there. You'll be hands-on, educating, and surfacing the risks and opportunities that leadership needs to make the bigger calls.
Key Responsibilities<ul><li>Educate and partner with engineering teams on working effectively with AI, establishing the patterns and best practices that help them adopt new capabilities safely.</li><li>Integrate AI across the software development lifecycle, embedding it into IDEs, code review, test generation, CI/CD pipelines, and documentation workflows, and connecting the tools used within our environment including Jira, Confluence, and GitHub.</li><li>Build the platform layer that enables teams to safely deploy and run agents.</li><li>Develop orchestration tooling that coordinates multi-agent workflows, manages state, and routes work across agents.</li><li>Create and maintain MCP servers, command line tools and prompt libraries that teams can safely build on.</li><li>Own the operational health, safety, and cost efficiency of AI systems in production.</li><li>Instrument AI systems for end-to-end observability in Datadog, capturing traces, token usage, latency, and cost at the span level so teams can see how agents and prompts behave in production and act on regressions before they reach users.</li><li>Build testing harnesses for non-deterministic systems that measure whether agent and prompt changes improve accuracy.</li></ul>
What You&rsquo;ll Bring
We know that great engineers come from many different backgrounds. If you meet most of these requirements, we'd love to hear from you:<ul><li>Production AI operations experience. You've shipped and operated real LLM or agentic AI systems in production, with hands-on exposure to observability, cost control, monitoring, and the failure modes that surface with real users.</li><li>Agentic system design. You understand multi-agent coordination, tool integration, and orchestration patterns (AWS Bedrock, AgentCore, or similar).</li><li>Cloud infrastructure comfort. You're comfortable shipping and operating on cloud platforms, with AWS experience essential.</li><li>Consultative problem-solving. You're at ease with fuzzy briefs, ask sharp questions, and help teams discover what they actually need rather than waiting for a spec.</li><li>Hands-on engineering experience in TypeScript or Python, comfortable with Terraform, containers, Kubernetes, CI/CD, and API design, so you can build and operate production-grade platform tooling rather than just prototype it.</li><li>Communication and teaching. You can explain complex AI concepts to engineers at any level and bring people along with you.</li><li>Evaluation framework experience. You've designed rigorous testing and measurement approaches for non-deterministic AI systems.</li></ul>
Personal Attributes<ul><li>Someone who lifts the people around them and enjoys sharing what they know.</li><li>A collaborator who values diverse opinions and voices.</li><li>Confident in decision-making, even under pressure.</li><li>Passionate about continuous improvement and engineering excellence.</li><li>Committed to creating safe, inclusive spaces where every team member can thrive.</li></ul></div>

Tata Consultancy Services

<div>
 
 
 Job Title
 
 &ndash; AI Platform Engineer
 
 
 
 Company
 
 &ndash; TCS (MEA)
 
 
 
 Location
 
 &ndash; Dubai
 
 
 
 Job type
 
 &ndash; Full time
 
 
 
 
 
 
 About Us:
 
 
 
 Tata Consultancy Services (TCS) is an IT services, consulting and business solutions organization that has been partnering with many of the world&rsquo;s largest businesses in their transformation journeys for over 50 years. TCS offers a consulting-led, cognitive powered, integrated portfolio of business, technology and engineering services and solutions. This is delivered through its unique Location Independent Agile&trade; delivery model, recognized as a benchmark of excellence in software development.
 
 
 A part of the Tata group, India's largest multinational business group, TCS has over 616,171 of the world&rsquo;s best-trained consultants with 157 nationalities in 53 countries. For more information, visit www.tcs.com and follow TCS news at @TCS_News.
 
 
 
 
 
 
 
 Job Description:
 
 
 
 
 
 
 
 
 Key Accountabilities:
 
 
 
 
 AI Platform Operations
 
 
 
 Provide day-to-day monitoring and operational support of Gernas, its dependent AI applications, deployed AI agents, agent runtime environments, AI gateways, and supporting Azure and AWS cloud services. Ensure continuous availability and consistent service performance across production environments, proactively identifying degradation signals before they become user-visible incidents. Maintain operational dashboards covering platform health, agent execution, LLM consumption, gateway throughput, and integration endpoints, and ensure that ownership of every production component is unambiguous and documented.
 
 
 
 Incident and Problem Management
 
 
 
 Lead L2 and L3 incident response across the Gernas platform and its surrounding AI products. Conduct structured root-cause analysis, drive service restoration within agreed P1&ndash;P4 service-level commitments, and coordinate major incidents involving multiple engineering, cloud, security, and vendor stakeholders. Maintain problem records for recurring or systemic issues, ensure that post-incident reviews are completed with clear corrective and preventive actions, and own the closure of those actions through to engineering remediation. Act as the bridge between the L1 AI Operations Centre and L3 engineering teams during complex incidents.
 
 
 
 AI Agent and Workflow Support
 
 
 
 Troubleshoot the behaviour of single-agent and multi-agent workflows, including agent orchestration, tool invocation, skill execution, prompt construction, memory and context handling, planning loops, and inter-agent coordination. Diagnose failures across Microsoft Agent Framework and LangGraph execution, including state-graph traversal, conditional routing, tool-call mismatches, retries, and human-in-the-loop checkpoints. Investigate integrations with enterprise systems, MCP-exposed tools, and downstream APIs, and work with engineering teams to harden agent designs where production data reveals weaknesses.
 
 
 
 LLM and Model Service Support
 
 
 
 Support the operational stability of the model-serving layer across Azure OpenAI PTU deployments, AWS Bedrock, and Core42 Compass. Monitor and troubleshoot PTU capacity utilisation, token consumption patterns, quota and throttling behaviour, latency profiles, content filtering outcomes, and model endpoint availability. Coordinate with the AI Hub team on routing decisions between frontier and in-region models, assess cost and capacity headroom, and manage capacity escalations and vendor tickets where service degradation originates upstream.
 
 
 
 AKS and Runtime Support
 
 
 
 Provide hands-on operational support of the Azure Kubernetes Service estate hosting Gernas agent runtimes and supporting services. Troubleshoot pod, deployment, service, ingress, namespace, secret, and configuration issues; investigate autoscaling behaviour and resource utilisation anomalies; and analyse container logs, network connectivity, and runtime failures. Work closely with the Cloud Platform team on cluster-level concerns, image security, node health, and platform upgrades, ensuring AKS-hosted workloads remain stable through patching and lifecycle events.
 
 
 
 API, AI Gateway and MCP Support
 
 
 
 Operate and troubleshoot the Azure API Management layer that serves as both the enterprise API gateway and the MCP Gateway for Gernas. Diagnose issues with API policies, authentication, authorisation, routing, rate limiting, quotas, caching, and backend connectivity. Provide deep support for MCP servers, MCP tools, and the MCP Gateway pattern &mdash; including tool discovery, schema validation, protocol-level failures, and the coordination between MCP clients hosted in agents and the underlying tool endpoints. Ensure that the gateway remains a secure, observable, and policy-compliant control plane for all AI traffic.
 
 
 
 Observability and Performance Management
 
 
 
 Use Comet Opik as the primary observability surface for agent and LLM execution, working with traces, prompts, agent execution paths, latency breakdowns, token usage, errors, and model quality indicators. Build and maintain operational dashboards, alert rules, and correlation views that combine Opik telemetry with Azure Monitor, Application Insights, Log Analytics, and CloudWatch data. Lead performance optimisation initiatives where trace evidence shows hotspots in prompts, tools, retrieval steps, or model selection, and ensure that observability coverage keeps pace with platform evolution.
 
 
 
 Voice AI Support
 
 
 
 Support the production operation of ElevenLabs-based voice AI capabilities, including speech generation, voice-agent connectivity, real-time audio session handling, and API consumption patterns. Investigate latency, audio quality, dropped sessions, and integration failures across the voice channel and its dependent platforms, and coordinate with ElevenLabs and integration partners on upstream issues.
 
 
 
 Release and Change Management
 
 
 
 Validate releases prior to and immediately following deployment, exercising production verification scripts, smoke tests, and rollback procedures. Maintain release readiness through clear configuration management, environment parity checks, and pre-deployment risk reviews. Operate within FAB's change-management framework, ensuring that all production changes including patches, upgrades, configuration adjustments, and model or prompt rotations pass through the appropriate change controls and post-implementation review.
 
 
 
 Security, Risk and Compliance
 
 
 
 Uphold the security and compliance posture of Gernas and dependent AI products. Manage identity and access controls, secrets, certificates, and managed identities across the platform; coordinate vulnerability remediation and patching cycles; and maintain audit evidence for internal audit, supervisory reviews, and external assurance. Operate responsible-AI controls including content filtering, PII and PCI detection, data egress controls, and model-access governance and ensure that secure integration patterns are followed across every API, MCP tool, and external dependency.
 
 
 
 Service Improvement and Automation
 
 
 
 Drive continuous reduction of manual support effort through automation of routine operational tasks, self-healing patterns, monitoring enhancements, and proactive remediation. Maintain a current and high-quality library of support playbooks, runbooks, knowledge articles, and standard operating procedures. Identify and lead service-improvement initiatives that lift platform reliability metrics, reduce incident volume, and shorten mean time to resolution.
 
 
 
 Stakeholder and Vendor Coordination
 
 
 
 Operate as a credible technical counterpart to business units, engineering teams, the Cloud Platform team, Cybersecurity, Architecture, AI Governance, and Service Management. Lead vendor engagement with Microsoft, AWS, Core42, ElevenLabs, and other technology partners on incidents, capacity reviews, roadmap items, and product issues, ensuring that vendor accountability is exercised and that escalations are progressed effectively.
 
 
 
 On-Call and Operational Readiness
 
 
 
 Participate in a 24&times;7 support model, including a structured on-call rotation, major incident leadership, disaster-recovery exercises, business-continuity testing, and production-readiness assessments for new agents, models, and integrations entering the platform. Treat operational readiness as a release gate rather than an afterthought, and ensure that nothing reaches production without explicit operational sign-off.
 
 
 
 
 
 
 TECHNICAL SKILLS: minimum 5-7 yrs of working experience mandatory
 
 
 
 
 Cloud AI Services
 
 
 
 
 
 
 
 Agentic AI Frameworks
 
 
 
 
 LLM Operations
 
 
 
 
 Kubernetes and Containers
 
 
 
 
 API and MCP Gateway
 
 
 
 
 Observability
 
 
 
 
 DevOps and Automation
 
 
 
 
 Voice AI
 
 
 
 
 Security and Governance
 
 
 
 
 Service Management
 
 
 
 
 
 
 
 KNOWLEDGE, SKILLS, &amp; EXPERIENCE
 
 
 
 
 Education
 
 
 
 Bachelor's degree in Computer Science, Artificial Intelligence, Information Technology, Engineering, or a closely related discipline. A relevant master's degree in AI, Machine Learning, or Cloud Computing will be considered an advantage.
 
 
 
 Professional Experience
 
 
 
 Approximately 5&ndash;7 years of overall IT experience, with significant time spent in cloud application support, platform engineering, DevOps, Site Reliability Engineering, production operations, or senior technical support roles. Of this, a minimum of 2&ndash;3 years of relevant experience supporting AI, machine learning, Generative AI, conversational AI, cloud-native platforms, or other data-intensive enterprise systems is required.
 
 
 
 Banking and Regulated Environment
 
 
 
 
 
 
 
 Technical Troubleshooting
 
 
 
 
 
 
 
 Operational Skills
 
 
 
 
 
 
 
 Communication and Leadership
 
 
 
 
 
 
 
 
 
 Thank you for your interest in applying for this position with TCS. We will review your application and will get back to you if we are considering your interest in this opportunity.
 
 
 
 
 
 
 Application Deadline: 30- July -2026
 
 
 
 
 
 
 
 Privacy Note:
 
 
 
 https://www.tcs.com/connect-with-tcs/privacy-policy
 
</div>

ClearRoute

<div>About Us
ClearRoute is an engineering consultancy bridging Quality Engineering, Cloud Platforms and Developer Experience. We help enterprises reliably bring high-impact digital products to market faster, cheaper, and safer, working with technology leaders facing complex business challenges.
We take as much pride in our people, culture and work-life balance as we do in making better software. We&rsquo;re not just making better software. We&rsquo;re making the making of software better. Collaborative, entrepreneurial and dedicated to problem solving, we bring the step change our customer need to sustain innovation. Our values challenge us to do the best we can for ClearRoute, our customers and most importantly our team. This is an opportunity for you to build the organisation from the ground up, use your voice to drive change and help transform organisations and problem domains.
The Role
We are looking for a Platform Engineer to join client-facing delivery teams and help design, build, and operate modern developer platforms. You will work across a range of industries and tech stacks, so adaptability matters as much as expertise.
On any given engagement you might be building a goldenpath CI/CD pipeline, hardening a Kubernetes cluster, migrating secrets management to Vault, or running a platform engineering workshop with a client's engineering teams. You will be expected to lead technical workstreams, pair with client engineers, and leave behind well-documented, production-ready infrastructure.
Increasingly, our clients are asking us to help them build the foundations for AI, from model-serving infrastructure and MLOps pipelines to safely integrating LLM powered tooling into existing developer workflows. You don't need to be an ML engineer, but you should be curious about this space and comfortable building the platform layer that makes AI workloads production ready.
What You'll Do
Platform &amp; Infrastructure<ul><li>Design and deliver internal developer platforms (IDPs) that improve developer experience and accelerate software delivery.</li><li>Build and maintain infrastructure-as-code using Terraform, Pulumi, or CDK and enforce code review and testing standards.</li><li>Manage and optimise Kubernetes clusters (EKS, GKE, AKS) including multi-tenancy, networking, RBAC, and cost controls.</li><li>Own CI/CD pipelines end-to-end: from source control policies through build, test, security scanning, artefact management, and deployment.</li><li>Implement secrets management and certificate lifecycle automation using HashiCorp Vault or equivalent.</li></ul>
Reliability &amp; Security<ul><li>Embed SRE practices: SLOs, error budgets, runbooks, on-call design, and blameless post-mortems.</li><li>Integrate security tooling (SAST, DAST, dependency scanning, policy-as-code) into delivery pipelines.</li><li>Design and test disaster-recovery strategies; automate them where possible.</li><li>Ensure compliance with client security standards and relevant regulatory frameworks.</li></ul>
AI &amp; Emerging Technology<ul><li>Design and operate infrastructure for AI/ML workloads: GPU node pools, model-serving runtimes (Triton, vLLM, BentoML), and vector database deployments (pgvector, Weaviate, Qdrant).</li><li>Build and maintain MLOps pipelines model training, versioning, evaluation, and promotion to production using platforms such as Kubeflow, MLflow, or cloud-native equivalents.</li><li>Integrate LLM APIs and AI agent frameworks into existing developer platforms, including prompt management, observability, cost controls, and rate-limit guardrails.</li><li>Advise clients on AI readiness: data infrastructure, governance, security controls (model access policies, output filtering), and the organisational changes that sit alongside the technical work.</li><li>Stay current with the fast-moving AI tooling landscape and bring relevant ideas back to the team and to clients.</li></ul>
Client Engagement<ul><li>Lead technical discovery sessions and platform assessments with client engineering and architecture teams.</li><li>Translate client requirements into clear technical plans and communicate trade-offs to both technical and non-technical stakeholders.</li><li>Coach and upskill client platform and application engineers through pairing, workshops, and code review.</li><li>Produce high-quality documentation, architecture decision records (ADRs), and runbooks that clients can own after the engagement.</li></ul>
What We're Looking For....
Essential<ul><li>Solid hands-on experience with at least one major cloud provider (AWS preferred; GCP or Azure accepted).</li><li>Production experience with Kubernetes and familiarity with the surrounding ecosystem (Helm, ArgoCD / Flux, Karpenter, Cilium, etc.).</li><li>Strong infrastructure-as-code skills, Terraform is the baseline; other tools are a bonus.</li><li>Practical experience designing or operating CI/CD systems (GitHub Actions, GitLab CI, Tekton, or similar).</li><li>Comfort working in Linux environments and writing automation in Python, Bash, or Go.</li><li>The ability to explain complex technical concepts clearly to a mixed audience.</li><li>A consulting mindset: you care about solving the client's actual problem, not just delivering a deliverable.</li></ul>
Ideal (not required)<ul><li>Hands-on experience with AI/ML infrastructure: GPU scheduling on Kubernetes, model-serving runtimes, or MLOps tooling (Kubeflow, MLflow, Ray, etc.).</li><li>Familiarity with LLM APIs (OpenAI, Anthropic, Bedrock, Vertex AI) and patterns for building reliable, observable AI-powered applications.</li><li>Experience with vector databases or semantic-search infrastructure.</li><li>Experience with event-streaming platforms such as Apache Kafka.</li><li>Familiarity with configuration management tools (Chef, Ansible, or similar).</li><li>Knowledge of mainframe environments or hybrid-cloud patterns.</li><li>Relevant certifications: CKA/CKAD, AWS Solutions Architect, HashiCorp Vault, etc.</li><li>Prior consultancy or client-facing delivery experience.</li></ul>
At ClearRoute, we believe diverse perspectives lead to better outcomes, and inclusion creates the conditions for everyone to thrive. We are proud to have built a family friendly working environment and have many employees who have caring responsibilities alongside work. We welcome applications from people who require flexibility and will be happy to discuss needs on an individual basis.
We are committed to fostering a culture where all team members feel respected, supported, and empowered to do their best work. We celebrate individuality and our differences and understand that some differences may mean that you require changes made to the interview process. We are happy to cater to your needs to make the interview accessible, if this is something you require please let us know by emailing us at join@clearroute.io</div>

Tibber

<div>We're at a pivotal moment for both Tibber and the planet. When joining Tibber, you won&rsquo;t just help scale a forward-thinking tech company &ndash; you&rsquo;ll contribute to a real shift in how people consume electricity. With millions of smart devices connected to the Tibber platform (like electric vehicles and smart thermostats), we have one of the most unique energy trading portfolios on the market.
We&rsquo;re looking for a Senior Ai Platform Engineer to join us in making the energy transition happen &ndash; maybe that&rsquo;s you?
Job Mission
As a Senior AI Platform Engineer, you&rsquo;ll join the Agentic Enablement side of our Platform team and help build the technical foundation for how Tibber adopts and scales agentic systems across engineering.
Your focus will be on the architecture, workflows and trust infrastructure that allow AI agents to safely support software development - from writing and testing code to reviewing changes, triggering workflows and knowing when to involve a human.
This is a platform role, which means the engineers building agents at Tibber are your users. Your success is measured by how quickly and confidently they can move from idea to production without needing you in the room.
This role is for someone who enjoys building reliable systems, thinking deeply about architecture and helping teams adopt new ways of working with confidence.
What you'll do
<ul><li>Contribute to the design and architecture of Tibber&rsquo;s agentic development platform, including orchestration layers, platform integrations, feedback loops and escalation paths, and the developer-facing CLI and onboarding experience.</li><li> 
Take ownership of key platform components, from design and implementation to reliability and continuous improvement.</li></ul><ul><li>
Help define workflows, quality standards and ownership models for how agents can write, test, review and support code.</li><li> 
Help shape safe and reliable agentic workflows, including human-in-the-loop boundaries.</li><li> 
Build trust infrastructure that makes agentic systems observable, secure, reversible and reliable in production.</li><li> 
Work closely with Staff Engineers and the wider Platform team to build on Tibber&rsquo;s AWS, Kubernetes and infrastructure foundations.</li><li> 
Bring people along through clear technical communication, thoughtful collaboration and strong engineering judgment.</li></ul>
What we're looking for
You are driven by impact and know how to turn strategy into execution. You're used to fast-paced environments and thrive when given clear goals and high accountability.
 
 
We believe you'll thrive here if you:<ul><li>
Bring hands-on production experience building and running reliable agentic software systems.</li><li> 
Treat evaluation as a first-class engineering concern, you&rsquo;ve built eval pipelines for non-deterministic systems, whether LLM-as-judge, regression suites, or golden datasets, and know that measuring agent quality in production is an ongoing discipline, not a launch checklist.</li><li> 
Bring hands-on production experience building and running reliable agentic software systems.</li><li> 
Have strong infrastructure knowledge across Kubernetes, Terraform, Infrastructure as Code, and platform networking.</li><li> 
Demonstrate strong software engineering depth in TypeScript, with the ability to own and operate services end-to-end.</li><li> 
Enjoys to communicate technical directions and collaborate with engineering teams to enable adoption, and turn platform capabilities into real impact.</li><li> 
Apply a strong security mindset to agent permissions, least-privilege designs, and blast radius awareness.</li><li> 
Have working knowledge of Model Context Protocol (MCP), evaluation frameworks, or cloud capabilities like AWS Bedrock.</li></ul>
&zwj;️&zwj;️The Tibber Mindset
Tibber exists to drive energy independence - away from fossil dependency. Our tech helps hundreds of thousands of households shift their electricity consumption to more sustainable and affordable hours - from electric vehicles to smart thermostats, all connected to the Tibber platform. We strengthen the resilience of the grid. Not in theory. In everyday life.
We win, fail, and grow together, staying curious and pushing for better every day. AI is shaping how we work - and we're on a journey to make it central to everything we do. The further we go, the more our Tibberinos matter - and yes, that's what we call ourselves!
Started by two founders in 2016. Now 300+ Tibberinos across Stockholm, Oslo, Berlin, Amsterdam, and F&oslash;rde. And we're still just getting started.
You're welcome at Tibber for who you are. We hire for what you can do and how you think - not your background or where you've been. Diverse teams build better products, and we mean that.</div>

Senior AI Platform Engineer

<div>
 
 About Us
 
 
 
 ClearRoute is an engineering consultancy bridging Quality Engineering, Cloud Platforms and Developer Experience. We help enterprises reliably bring high-impact digital products to market faster, cheaper, and safer, working with technology leaders facing complex business challenges.
 
 
 We take as much pride in our people, culture and work-life balance as we do in making better software. We&rsquo;re not just making better software. We&rsquo;re making the making of software better. Collaborative, entrepreneurial and dedicated to problem solving, we bring the step change our customer need to sustain innovation. Our values challenge us to do the best we can for ClearRoute, our customers and most importantly our team. This is an opportunity for you to build the organisation from the ground up, use your voice to drive change and help transform organisations and problem domains.
 
 
 
 The Role
 
 
 
 We are looking for a Platform Engineer to join client-facing delivery teams and help design, build, and operate modern developer platforms. You will work across a range of industries and tech stacks, so adaptability matters as much as expertise.
 
 
 On any given engagement you might be building a goldenpath CI/CD pipeline, hardening a Kubernetes cluster, migrating secrets management to Vault, or running a platform engineering workshop with a client's engineering teams. You will be expected to lead technical workstreams, pair with client engineers, and leave behind well-documented, production-ready infrastructure.
 
 
 Increasingly, our clients are asking us to help them build the foundations for AI, from model-serving infrastructure and MLOps pipelines to safely integrating LLM powered tooling into existing developer workflows. You don't need to be an ML engineer, but you should be curious about this space and comfortable building the platform layer that makes AI workloads production ready.
 
 
 
 What You'll Do
 
 
 
 
 Platform &amp; Infrastructure
 
 
 
 <ul>
 <li>
 Design and deliver internal developer platforms (IDPs) that improve developer experience and accelerate software delivery.
 </li>
 <li>
 Build and maintain infrastructure-as-code using Terraform, Pulumi, or CDK and enforce code review and testing standards.
 </li>
 <li>
 Manage and optimise Kubernetes clusters (EKS, GKE, AKS) including multi-tenancy, networking, RBAC, and cost controls.
 </li>
 <li>
 Own CI/CD pipelines end-to-end: from source control policies through build, test, security scanning, artefact management, and deployment.
 </li>
 <li>
 Implement secrets management and certificate lifecycle automation using HashiCorp Vault or equivalent.
 
 
 </li>
 </ul>
 
 Reliability &amp; Security
 
 
 
 <ul>
 <li>
 Embed SRE practices: SLOs, error budgets, runbooks, on-call design, and blameless post-mortems.
 </li>
 <li>
 Integrate security tooling (SAST, DAST, dependency scanning, policy-as-code) into delivery pipelines.
 </li>
 <li>
 Design and test disaster-recovery strategies; automate them where possible.
 </li>
 <li>
 Ensure compliance with client security standards and relevant regulatory frameworks.
 
 
 </li>
 </ul>
 
 AI &amp; Emerging Technology
 
 
 
 <ul>
 <li>
 Design and operate infrastructure for AI/ML workloads: GPU node pools, model-serving runtimes (Triton, vLLM, BentoML), and vector database deployments (pgvector, Weaviate, Qdrant).
 </li>
 <li>
 Build and maintain MLOps pipelines model training, versioning, evaluation, and promotion to production using platforms such as Kubeflow, MLflow, or cloud-native equivalents.
 </li>
 <li>
 Integrate LLM APIs and AI agent frameworks into existing developer platforms, including prompt management, observability, cost controls, and rate-limit guardrails.
 </li>
 <li>
 Advise clients on AI readiness: data infrastructure, governance, security controls (model access policies, output filtering), and the organisational changes that sit alongside the technical work.
 </li>
 <li>
 Stay current with the fast-moving AI tooling landscape and bring relevant ideas back to the team and to clients.
 
 
 </li>
 </ul>
 
 Client Engagement
 
 
 
 <ul>
 <li>
 Lead technical discovery sessions and platform assessments with client engineering and architecture teams.
 </li>
 <li>
 Translate client requirements into clear technical plans and communicate trade-offs to both technical and non-technical stakeholders.
 </li>
 <li>
 Coach and upskill client platform and application engineers through pairing, workshops, and code review.
 </li>
 <li>
 Produce high-quality documentation, architecture decision records (ADRs), and runbooks that clients can own after the engagement.
 
 
 </li>
 </ul>
 
 Essential
 
 
 
 
 What We're Looking For....
 
 
 
 <ul>
 <li>
 Solid hands-on experience with at least one major cloud provider (AWS preferred; GCP or Azure accepted).
 </li>
 <li>
 Production experience with Kubernetes and familiarity with the surrounding ecosystem (Helm, ArgoCD / Flux, Karpenter, Cilium, etc.).
 </li>
 <li>
 Strong infrastructure-as-code skills, Terraform is the baseline; other tools are a bonus.
 </li>
 <li>
 Practical experience designing or operating CI/CD systems (GitHub Actions, GitLab CI, Tekton, or similar).
 </li>
 <li>
 Comfort working in Linux environments and writing automation in Python, Bash, or Go.
 </li>
 <li>
 The ability to explain complex technical concepts clearly to a mixed audience.
 </li>
 <li>
 A consulting mindset: you care about solving the client's actual problem, not just delivering a deliverable.
 
 
 </li>
 </ul>
 
 Ideal (not Required)
 
 
 
 <ul>
 <li>
 Hands-on experience with AI/ML infrastructure: GPU scheduling on Kubernetes, model-serving runtimes, or MLOps tooling (Kubeflow, MLflow, Ray, etc.).
 </li>
 <li>
 Familiarity with LLM APIs (OpenAI, Anthropic, Bedrock, Vertex AI) and patterns for building reliable, observable AI-powered applications.
 </li>
 <li>
 Experience with vector databases or semantic-search infrastructure.
 </li>
 <li>
 Experience with event-streaming platforms such as Apache Kafka.
 </li>
 <li>
 Familiarity with configuration management tools (Chef, Ansible, or similar).
 </li>
 <li>
 Knowledge of mainframe environments or hybrid-cloud patterns.
 </li>
 <li>
 Relevant certifications: CKA/CKAD, AWS Solutions Architect, HashiCorp Vault, etc.
 </li>
 <li>
 Prior consultancy or client-facing delivery experience.
 
 
 </li>
 </ul>
 
 At ClearRoute, we believe diverse perspectives lead to better outcomes, and inclusion creates the conditions for everyone to thrive. We are proud to have built a family friendly working environment and have many employees who have caring responsibilities alongside work. We welcome applications from people who require flexibility and will be happy to discuss needs on an individual basis.
 
 
 
 
 We are committed to fostering a culture where all team members feel respected, supported, and empowered to do their best work. We celebrate individuality and our differences and understand that some differences may mean that you require changes made to the interview process. We are happy to cater to your needs to make the interview accessible, if this is something you require please let us know by emailing us at
 
 
 join@clearroute.io
 
</div>

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