Trainee AI Engineer (m/w/d)

tem

📈 Who We Are: We are rebuilding the energy transaction, making it transparent and fair. Our goal is to put power back where it belongs, in the hands of customers and to take on one of the most critical problems of our century, access to low cost electricity. tem exists to fix a broken global energy market that’s long favoured legacy operators, intermediaries, and opaque pricing. Today’s electricity system was not designed for rapid decarbonisation, AI-driven efficiency or fair access for the actual users - businesses and generators. We’ve built the first AI native transaction infrastructure to reinvent how electricity is bought, sold and priced. Our technology is designed to cut out the inefficient fees, automate complex market flows, and bring transparency and fairness to energy transactions at scale. In late 2025, after extraordinary growth, we closed a $75 million Series B - led by Lightspeed Venture Partners with participation from Albion, Atomico, Allianz, Hitachi Ventures, Hitachi Ventures, Schroders Capital and others - positioning us for global expansion, deeper product innovation and category leadership. We’re scaling internationally and building toward a future where AI-driven infrastructure is foundational to electricity markets worldwide. Since launch, our modern utility product, known as RED, has already facilitated thousands of business customers and billions in energy transaction value, proving that modern software and AI can transform an industry built on legacy systems. At tem, we’re not just building another energy company, we’re rearchitecting market infrastructure so that transparency, efficiency and sustainability become the default, not the exception. 🏅 The Role: Rosso is tem's core IP, the transaction infrastructure that prices electricity for thousands of businesses, balances portfolios in real time, and sits on the critical path for every deal tem closes. The machine learning models inside Rosso forecasting, pricing, and optimisation are what make those decisions possible. Every inference shapes the prices our customers see. Today, tem's ML platform has solid foundations: Metaflow for orchestration, AWS Batch for compute, and automated CI/CD pipelines already in place. That's got Rosso to where it is. But as the number of model types grows and Rosso scales, the platform needs the next layer: structured experiment tracking, a model registry, production monitoring, and self-service tooling that lets ML engineers move at pace without being blocked on infrastructure. This role exists to build that layer and define what the platform looks like at scale. You will join the Rosso service alongside a Senior MLOps Engineer in a cross-functional team of ML engineers and software engineers. The destination is a platform that is genuinely self-service: ML engineers can run experiments, compare models, and ship to production without external intervention. It needs to scale across long-horizon forecasting tasks, real-time pricing engines, and large-scale optimisation workloads — not just the models that exist today. The concrete work ahead is specific: experiment tracking and a model registry are not yet in place. Backtesting infrastructure critical to the forecasting mission needs to be built. Shadow deployments will allow new models to be validated in production before they go live. And the platform needs to be designed for heterogeneous workloads, not just the models that exist today. This is a technical leadership role: you'll define the platform strategy and set the direction for the MLOps, while remaining hands-on in the most critical architectural decisions. The right person has seen ML platforms scale well and has learned from the times they haven't. You'll bring that judgment to a platform that can't afford expensive detours. 🚀 Responsibilities: * Own the ML platform strategy: Define the roadmap from Level 1 to Level 2, making architectural decisions ahead of when they'd otherwise become blockers. Keep the platform aligned to Rosso's commercial trajectory. * Build the foundations: Lead the design and build of experiment tracking, model registry, automated pipeline infrastructure, and production monitoring across all model types. * Deliver backtesting and shadow deployments: Build the infrastructure the forecasting and pricing teams need to validate models reliably against historical data and in production before they go live. * Set technical direction: Provide the architectural vision and standards the Senior MLOps Engineer executes against. This is a force-multiplier relationship, not a management one. * Partner across the team: Work closely with ML engineers and software engineers to understand what the platform needs to unlock the next wave of Rosso capabilities. Translate those needs into principled platform decisions. * Choose the right tools: Evaluate the MLOps tooling ecosystem with clear eyes. Make choices that fit tem's scale and workload mix not what's fashionable. * Drive deployment reliability: Push toward more frequent, reliable model deployment cycles as Rosso moves from batch-heavy workflows toward live, near-real-time processes. * Define best practices: Establish standards for how models are trained, versioned, deployed, and monitored across the team. Create a platform ML engineers trust. What success looks like: * MLOps is no longer a bottleneck, ML engineers are unblocked to focus on model quality * The time to deploy new machine learning models goes from days to minutes * The core features required from the machine learning platform are delivered before they block progress e.g. backtesting and experiment tracking 🎯 Requirements: Must-Haves: * Scaled an ML platform from early-stage: Demonstrable experience taking an ML platform from early stages to best-in-class infrastructure at a fast-moving company. You've been there, done it, and you're comfortable with the messiness and ambiguity that comes with scale-up life. * ML pipeline expertise: Deep experience across the whole MLOps lifecycle with ML pipeline orchestration (Metaflow, Prefect, Airflow or equivalent) and ML infrastructure (Sagemaker, Vertex AI, Chalk, or equivalent). * Model lifecycle tooling: Hands-on experience building or operating experiment tracking systems (MLflow, W&B, or similar), model registries, and governance tooling for model fleets at scale. Knows what good looks like and what to avoid. * Broad MLOps tooling knowledge: Across the ecosystem monitoring, drift detection, CI/CD for ML, containerisation, IaC (Terraform, AWS CDK). Able to evaluate trade-offs and make principled choices for a specific context, not just default to what they know. * Technical leadership track record: Evidence of setting platform direction, influencing cross-functional teams, and defining standards at Staff+ level. Raises the quality bar through design reviews, code reviews, and mentoring. Knows when to drive strategy and when to get into the weeds. * Heterogeneous workload experience: Experience designing and operating platforms serving heterogeneous workloads (e.g. forecasting, classification, operations research, etc), not just one model type across batch and real time applications. * Python, AWS + IaC: Strong Python; hands-on experience with AWS and infrastructure-as-code (Terraform, AWS CDK). Bonus points: * Worked in a role where ML is at the core of the product * Familiarity with Metaflow specifically * Experience with operations research, large-scale optimisation in a production context * Experience working with business critical time series forecasting models * Exposure to reinforcement learning in a production setting * Exposure to production LLM workloads e.g. fine tuning 🗣️ Interview Process: Our processes normally take around 2-3 weeks from first call to offer - please let us know about any adjustments to timelines that may be required. 1. First call with our Talent Team (30 mins). This is to understand your experience, motivations, and discuss the role in more detail. 2. Behaviour Interview with Tim, Head of Data (60 mins). This is your chance to really understand the role, the expectations, and ensure alignment on ways of working. 3. Technical Interview with the Team (90 mins). You'll meet with potential peers in this session and work through a live technical exercise. 4. Culture-Add Interview with Stakeholders (45 mins). The final session will be with two cross-functional stakeholders, and will explore how your values align with ours, and is designed to be a genuine two-way conversation, your chance to understand what it's really like to work at tem. We welcome applications from people of all backgrounds, experiences, and identities, including those that are traditionally underrepresented in the tech and energy sectors. If you’re excited about this role but not sure you meet every requirement, we’d still love to hear from you. Your unique perspective could be exactly what we’re looking for.

Ultra Tendency

ABOUT THE ROLE We are looking for an experienced IT Trainer & Data Engineer who combines deep technical expertise with a passion for knowledge sharing. In this hybrid role, you will design and deliver high-impact technical training programs while also contributing to client-facing Data Engineering and Cloud projects. You will work with modern data and cloud technologies including Databricks, Apache Spark, Kafka, Kubernetes, Terraform, and leading cloud platforms. When not delivering training, you will support customers in designing, building, and optimizing scalable data platforms and engineering solutions. This role is ideal for someone who enjoys both hands-on technical work and helping others grow their skills. ---------------------------------------------------------------------------------------------------------------------------------- WHAT YOU'LL DO TECHNICAL TRAINING & ENABLEMENT * Design and deliver instructor-led training for clients and internal teams. * Create engaging learning materials, labs, workshops, and certification preparation content. * Translate complex technical concepts into practical, business-relevant learning experiences. * Continuously update training content to reflect industry trends and emerging technologies. * Mentor learners and support their technical development. DATA ENGINEERING & CONSULTING * Design, build, and optimize modern data platforms and data pipelines. * Develop batch and streaming solutions using technologies such as Databricks, Spark, Kafka, and cloud-native services. * Support customers in data architecture, platform modernization, and cloud transformation initiatives. * Implement Infrastructure-as-Code and automation using Terraform and CI/CD practices. * Collaborate with architects, engineers, and customer stakeholders to deliver successful projects. ---------------------------------------------------------------------------------------------------------------------------------- WHAT WE'RE LOOKING FOR TECHNICAL EXPERTISE * Strong experience in Data Engineering, Data Platforms, or Cloud Engineering. * Hands-on experience with Databricks, Apache Spark, Kafka, or similar technologies. * Experience with at least one major cloud platform (Azure, AWS, or GCP). * Understanding of modern data architectures, including Data Lakes, Lakehouses, and Data Warehouses. * Familiarity with Kubernetes, Docker, Terraform, and DevOps practices is highly desirable. * Strong SQL and data modeling skills. TRAINING & COMMUNICATION * Experience delivering technical training, workshops, mentoring, or enablement programs. * Ability to explain complex concepts to both technical and non-technical audiences. * Excellent presentation, facilitation, and stakeholder management skills. * Passion for learning and helping others develop their capabilities. PERSONAL QUALITIES * Customer-focused and consultative mindset. * Strong problem-solving and analytical skills. * Self-motivated with the ability to work independently and collaboratively. * Curious, adaptable, and eager to stay current with emerging technologies. * Excellent communication and presentation skills in English & German. ---------------------------------------------------------------------------------------------------------------------------------- WHAT WE OFFER * Opportunity to work on cutting-edge Cloud, Data, and AI projects. * A unique combination of technical consulting and professional training. * Continuous learning, certifications, and professional development. * Flexible working arrangements and modern tooling. * International and collaborative work environment. * Clear opportunities for career growth and specialization. ---------------------------------------------------------------------------------------------------------------------------------- WHY JOIN US? You'll have the opportunity to influence both technology adoption and people development. Rather than focusing solely on delivery or training, you'll play a key role in helping organizations build modern data capabilities while empowering their teams to succeed. ABOUT US Ultra Tendency is an international Data Engineering consultancy specializing in Big Data, Cloud, Streaming, IIoT, and Microservices. Since 2010, we have helped leading organizations—including the European Central Bank, Deutsche Telekom, and Europe’s largest automotive manufacturer—build and operate large-scale, data-driven platforms. With 8 offices across 10 countries and a growing global team, we combine deep technical expertise with a strong commitment to open source. Our consultants actively contribute to projects such as Apache Kafka, Apache NiFi, Terraform, and Ansible, and we are a trusted Databricks partner. At Ultra Tendency, you'll work on challenging projects that push the boundaries of Data and AI while collaborating with talented colleagues in a culture built on learning, knowledge sharing, and mutual support. We evaluate candidates based on skills, experience, and business fit, and welcome applicants from all backgrounds. Data privacy statement: Data Protection for Applicants – Ultra Tendency

FAST LTA

KURZINFO Du denkst wie ein Ingenieur, fragst wie ein Detektiv und willst am Ende wissen: Läuft das wirklich zuverlässig? Bei uns baust du QA für eine lokale KI-Plattform auf, die in echten Rechenzentren läuft. DAS SIND DEINE AUFGABEN: * Manuelle Software- und Hardwaretests vor Ort durchführen * Teststrategien definieren mit QA-Tools wie TestRail, Xray oder Testrun * Transparente QA-Prozesse aufbauen – von Testmanagement bis zu aussagekräftigen Release-Reports * Fehlerberichte im Bugtracker (z. B. Jira) erstellen und pflegen * Fehlerbehebungen in der Software verifizieren * Eng mit dem AI-Developer Team zusammenarbeiten DAS BRINGST DU MIT: * Technisches Fundament: Studium (Informatik, Ingenieurwesen o. ä.) oder gleichwertige Praxis * QA-Erfahrung ≥ 3 Jahre – idealerweise in Backend-, Infra- oder Appliance-Umgebungen * Linux & Python – sicher im Shell-Workflow, pytest-Erfahrung erwünscht * QA-Tools: TestRail, Xray oder ähnliche Testmanagement-Tools, Jira für Ticketing * Netzwerke, APIs & Container – Docker-Kenntnisse vorhanden * CI/CD: Routine mit GitHub Actions oder Jenkins  * Kommunikation: Du übersetzt Technik in klare Qualitätskriterien – auf Deutsch und Englisch DAS BIETEN WIR: * Attraktives Gehalt und Zukunftssicherheit: Betriebliche Altersvorsorge (ETF basiert) mit 30% Arbeitgeberzuschuss * Flexibles Arbeiten: Flexible Arbeitszeiten und Home-Office-Option nach der Einarbeitung * Team & Kultur: Wertschätzende Zusammenarbeit, kurze Entscheidungswege und regelmäßige Team-Events (z.B. Wiesn, Weihnachtsfeier, Afterwork)  * Sport und Gesundheit - EGYM Wellpass: regelmäßige Sportangebote wie Yoga, Pilates und professionelles Lauftraining im Park mit externen Trainer:innen * Mobilität: JobRad und kostenlose Parkplätze  * Familienfreundlich: Zuschüsse zur Geburtsbeihilfe und Kinderbetreuung  * Klassiker inklusive: Frisches Obst und Getränke im Büro * Moderner Arbeitsplatz: Neues Office in München Mittersendling (S-Bahn Nähe) ab Juni 2026 ANSPRECHPARTNER*IN Bist du bereit für eine neue Herausforderung in einem dynamischen Umfeld? Dann freuen wir uns auf deine Bewerbung!   Schick uns einfach deinen Lebenslauf – und wenn du stolze Testframeworks oder Repos hast, gerne dazu. Wir melden uns bei dir!