(junior) cloud site reliability engineer (network) (m/f/x)

(junior) full-stack software engineer - supply chain management applications (m/f/d)

(senior) cloud engineer / linux-spezialist (m/w/d)

ai engineer (m/f/d)

full stack engineer

forward deployed engineer

frontend engineer

platform engineer

ai engineer

Control Risks

<div>As Senior AI Engineer you will own the AI and data layer of the platform. You will design and implement the knowledge graph architecture, build agentic pipelines that enrich entity data through AI web research, and develop the graph interrogation layer that translates natural language compliance questions into structured graph queries.
You will work closely with the CTO and a small AI team including a data engineer and a junior AI engineer. You will have significant technical autonomy and direct influence over how the product evolves.
Please submit yoru CV in English. 
Requirements 
 
What You&rsquo;ll Work On
Knowledge graph architecture and implementation<ul><li>Own and evolve the platform&rsquo;s knowledge graph data model &mdash; entities (Company, Person, Community, Episodic), relationships, and temporal attributes</li><li>Implement and extend custom entity and relationship extraction pipelines using LMs, with structured output validation and confidence scoring</li><li>Use Graphiti (Zep AI&rsquo;s temporal knowledge graph framework) as the memory layer, managing episode ingestion, entity resolution, and graph updates</li><li>Design and enforce graph schema standards, ensuring consistency across data sources and ingestion pipelines</li><li>Build evaluation frameworks to measure extraction quality, entity disambiguation accuracy, and graph coverage</li></ul>
Agentic AI pipelines<ul><li>Design and implement multi-step agentic workflows that orchestrate LLM calls, web search, database lookups, and graph writes</li><li>Integrate AI web search (Tavily, Perplexity, or similar) as a tool within agentic pipelines for real-time entity enrichment</li><li>Build retrieval-augmented generation (RAG) pipelines over the knowledge graph, translating compliance queries into Cypher and natural language answers</li><li>Implement the graph interrogation layer &mdash; a conversational interface for compliance analysts to query the knowledge graph without writing Cypher</li><li>Manage LLM API integrations (Anthropic Claude, Gemini) including prompt engineering, structured outputs, and cost/latency optimisation</li></ul>
Data pipelines and integrations<ul><li>Build and maintain Python-based data ingestion pipelines reading from Databricks, connecting to external APIs (sanctions lists, corporate registries, Polixis, Vantage), and writing to Neo4j</li><li>Implement NER (named entity recognition) and entity disambiguation logic for extracting structured facts from unstructured compliance documents</li><li>Develop topic modelling pipelines to classify extracted facts as knowledge graph relationship attributes</li><li>Integrate with the .NET backend via well-defined HTTP contracts, ensuring the AI layer is independently deployable and testable</li></ul>
Research and technical leadership<ul><li>Stay current with the fast-moving agentic AI and knowledge graph literature &mdash; evaluate new frameworks, models, and techniques for production applicability</li><li>Contribute to architectural decisions with evidence: benchmarks, prototypes, and documented trade-offs</li><li>Maintain Claude Code context files (CLAUDE.md, SKILLS.md) for the AI codebase, enabling AI-augmented development across the team</li><li>Write clear technical documentation in Notion covering design decisions, data models, and operational runbooks.</li></ul>
What We&rsquo;re Looking For
Must-have<ul><li>3+ years of professional experience in AI/ML engineering, with at least 1 year working on knowledge graphs or graph-based data systems in production</li><li>Hands-on experience with Neo4j &mdash; data modelling, Cypher query writing, schema design, and performance tuning</li><li>Deep familiarity with LLM APIs (OpenAI, Anthropic, or Gemini) &mdash; prompt engineering, structured outputs, function/tool calling, and cost management</li><li>Understanding the trade-offs between LLMs and smaller fine-tuned models (Hugging Face Transformers, sentence-transformers) &mdash; knowing when a purpose-built model outperforms a prompted general one on cost, latency, and accuracy</li><li>Experience building agentic pipelines &mdash; multi-step LLM workflows with tool use, memory, and state management</li><li>Python proficiency &mdash; you write clean, testable Python and understand async patterns for I/O-bound AI workloads</li><li>Experience with RAG architectures &mdash; vector search, hybrid retrieval, and integrating retrieval into LLM-powered applications</li><li>Ability to read and apply AI research papers &mdash; you can take a paper on narrative extraction or entity disambiguation and turn it into a working prototype</li><li>Strong English communication skills &mdash; written and verbal</li></ul>
Strong advantage<ul><li>Direct experience or knowledge with Graphiti (Zep AI) or other temporal knowledge graph frameworks</li><li>Familiarity with entity resolution and disambiguation techniques at scale</li><li>Experience with NER pipelines, topic modelling, or information extraction from compliance or financial documents</li><li>Knowledge of the third-party risk, sanctions screening, or KYC/AML domain</li><li>Experience with Databricks, Delta Lake, or similar data lakehouse platforms</li><li>Exposure to GCP (Vertex AI, Cloud Run, Pub/Sub, Cloud Storage)</li><li>Experience designing evaluation frameworks for LLM-generated structured outputs</li><li>Prior work in a RegTech, FinTech, or compliance-adjacent environment</li></ul>
What Good Looks Like in This Role
We are direct about what we expect. The person who thrives here will:<ul><li>Treat the knowledge graph data model as the product&rsquo;s most important artefact &mdash; they think carefully about schema decisions and document them</li><li>Read the literature. When evaluating a new approach to entity disambiguation or graph memory, they check what&rsquo;s been published, not just what&rsquo;s on GitHub</li><li>Write evaluation harnesses before scaling pipelines. They know that a pipeline that produces plausible-looking output is not the same as one that produces correct output</li><li>Communicate blockers and trade-offs early. Technical uncertainty is expected &mdash; silence is not</li></ul></div>

Senior AI Engineer

SmartBroker

<div><h4 class="jobSectionHeader">Your mission</h4>
For our significantly growing Data, AI &amp; MarketingTech Department, we are looking for several AI Engineers. Our new data team is established to focus on GenAI use case development for client facing product features as well as for increasing efficiencies of internal processes among other data related topics. 
As AI Engineer you are playing a pivotal role in Smartbroker&rsquo;s data &amp; AI journey. You will work at the core of GenAI implementation at the intersection of business, data, and technology, drive innovation and data &amp; AI adoption throughout the organization. Our GenAI use cases are focused on generating significant business value through the usage of most modern AI technologies. 
 
Job Description 
<ul><li>
Design and develop AI use cases for internal processes or client facing product features using up to date GenAI technologies based on LLMs &amp; RAG</li><li>
Develop and maintain the technical AI and machine learning infrastructure for deploying AI-enabled solutions in the cloud</li><li>
Build AI and data features in close collaboration with product engineering teams</li><li>
Implement end-to-end efficient data and AI pipelines according to our data consumers&lsquo; needs up to consumable data &amp; AI products</li><li>
Own the technical implementation and components of your team as well as their operation and maintenance</li><li>
Act as a consultant for other technical teams with regards to usage of GenAI models, tools, and agents</li></ul><h4 class="jobSectionHeader">
Your profile</h4><ul><li>
University degree in a technical field: computer science, business informatics, natural sciences, or similar</li><li>
Expert knowledge on state-of-the-art AI use cases, agentic applications and best practices</li><li>
Expert knowledge in machine learning, generative AI, LLMs &amp; RAG, AI agents and related technical tools (OpenAI, LangChain , Langfuse &hellip;)</li><li>
Very good python &amp; SQL programming skills, Java is a plus</li><li>
Deep technical knowledge of modern cloud based AI platforms (GCP/AWS)</li><li>
Deep knowledge of software engineering best practices&mdash;from requirements specification, version control, CI/CD, and testing to deployment and monitoring of data pipelines and services</li><li>
Familiarity with data &amp; AI security, data regulations, data privacy requirements</li><li>
Proactive, solution-oriented mindset, strong sense of ownership</li><li>
Strong English communication and presentation skills, German is a plus</li><li>
Interest in finance and fintech industry and a sense of humor</li></ul><h4 class="jobSectionHeader">
About us</h4>
Among other things, the Smartbroker Group operates Smartbroker+ - a multi-award-winning online broker that is the only provider in Germany to combine the extensive product range of traditional brokers with the extremely favourable conditions of neobrokers. The portfolio also includes the digital fund broker SMARTBROKER Finance (formerly: FondsDISCOUNT.de), while the Group also operates four high-reach stock market portals (wallstreet-online.de, boersenNews.de, FinanzNachrichten.de and ARIVA.de). With several hundred million monthly page impressions, the Group is the largest independent financial portal operator in the German-speaking world and also maintains the largest financial community. 
The company is one of the pioneers of digital investment. For more than 25 years, we have been committed to breaking down the barriers to investing and enabling user-friendly and cost-effective investment processing. 
 
Our employees are the key to this success. Due to our strong growth momentum, we have created numerous attractive jobs in recent years. Every day, we strive to build the best team and give our employees the space to develop themselves in the best possible way. A diverse team of top performers ensures that we all learn something new every day. From a small team of around 20 people, we have now grown into a company with around 260 employees (as of 31 December 2023), based in a modern office in Berlin-Kreuzberg. 
 
We see ourselves as an innovative fintech that is embedded in a successful group of companies and thus combines a personal working atmosphere with the advantages of a financially strong public limited company. We rely on flat hierarchies, individual development and agile processes in all parts of the company. We are proud of the personal cohesion within the team and the above-average length of service of many employees. We are committed to diversity and equal opportunities. We strive to build a team that represents a variety of backgrounds, perspectives and skills. The more inclusive we are, the better our work and our team will be. 
 
If you want to be part of this fast-growing organisation, apply now to join the Smartbroker Group. 
 
We encourage women, non-binary and transgender people as well as all members of the LGBTQI+ community, people from marginalised ethnic backgrounds and people with visible or non-visible disabilities to apply if they are looking for an inclusive, diverse and safe working environment.</div>

AI Engineer (m/f/d)

Raisin

<div>
 Raisin is the world&rsquo;s leading platform for savings and investment products. Founded in 2012, the
 
 FinTech
 
 connects consumers with banks in the EU, the UK and the US. This gives consumers
 
 better interest rates
 
 and banks a
 
 diversified form of refinancing
 
 . Our vision is to offer savings and investments without barriers and thus open up the global 160 trillion euro market.
 
 
 Raisin currently employs more than 800 people from over 75 countries worldwide. Today, the platform holds over 80 billion euros in assets from
 
 more than one million investors
 
 which have accrued over 5 billion euros in returns.
 
 
 
 Team
 
 
 
 The AI Enablement team supports the adoption of AI capabilities across Raisin&rsquo;s products and internal systems.
 
 
 The team focuses on establishing a consistent and reliable approach to using AI in production by providing shared foundations, guidance, and reusable solutions. This includes working with engineering teams to integrate AI into existing systems, ensuring solutions are scalable, maintainable, and aligned with security and regulatory requirements.
 
 
 The scope ranges from improving internal workflows to enabling new product capabilities, depending on where AI can deliver measurable value.
 
 
 
 Your Responsibilities
 
 
 
 As an AI Engineer, you will help shape how AI is applied across the organization by combining hands-on engineering with enablement and guidance.
 
 
 
 Enable teams to adopt AI effectively
 
 
 
 <ul>
 <li>
 Work with product and engineering teams to identify and refine AI use cases
 </li>
 <li>
 Support teams in integrating AI capabilities into their systems and workflows by providing clear guidance on patterns, trade-offs, and practical implementation approaches
 </li>
 <li>
 Promote consistent and reusable solutions to reduce fragmentation across teams
 
 
 </li>
 </ul>
 
 Contribute To Shared Capabilities Where Needed
 
 
 
 <ul>
 <li>
 Develop or extend shared components, integrations, or services when required to unblock teams
 </li>
 <li>
 Define and implement patterns for integrating LLM capabilities into the platform
 </li>
 <li>
 Contribute to shared components or lightweight services that simplify access to AI capabilities across teams
 </li>
 <li>
 Manage AI costs and budget usage when providing shared model access and services to other teams.
 </li>
 <li>
 Support teams in developing retrieval-based approaches that make internal knowledge accessible to AI-driven use cases
 
 
 </li>
 </ul>
 
 Guide technical decisions
 
 
 
 <ul>
 <li>
 Evaluate tools, platforms, and approaches with consideration for scalability, security, and maintainability
 </li>
 <li>
 Help teams choose appropriate solutions based on context rather than applying AI by default
 </li>
 <li>
 Contribute to establishing a pragmatic and consistent approach to AI usage
 
 
 </li>
 </ul>
 
 Ensure robustness and responsible usage
 
 
 
 <ul>
 <li>
 Optimize AI system efficiency by making principled model selections based on cost-performance trade-offs, inference latency, and token usage optimization
 </li>
 <li>
 Support the implementation of safeguards such as access controls, validation, and monitoring
 </li>
 <li>
 Collaborate with Security, Legal, and other stakeholders on requirements related to AI usage
 </li>
 <li>
 Contribute to practices that ensure solutions are reliable and aligned with internal standards
 
 
 </li>
 </ul>
 
 Your Profile
 
 
 
 
 Background
 
 
 
 <ul>
 <li>
 Strong experience as a Software Engineer (5+ years), with focus on backend or platform development
 </li>
 <li>
 Strong generalist mindset, comfortable working across technical domains
 </li>
 <li>
 Experience working on production systems and collaborating across teams
 </li>
 <li>
 Comfortable operating in environments with evolving scope and priorities
 
 
 </li>
 </ul>
 
 Technical Skills
 
 
 
 <ul>
 <li>
 Proficiency in at least one programming language (Python preferred; JVM languages also relevant)
 </li>
 <li>
 Experience with cloud environments (AWS preferred)
 </li>
 <li>
 Solid understanding of APIs, system integration, and distributed systems
 
 
 </li>
 </ul>
 
 AI Experience
 
 
 
 <ul>
 <li>
 Practical experience working with AI tools or platforms (e.g. LLM APIs or similar technologies)
 </li>
 <li>
 Familiarity with concepts such as prompt design, context engineering, retrieval-based approaches, or AI-driven workflows
 </li>
 <li>
 Ability to assess where AI adds value and where it does not
 
 
 </li>
 </ul>
 
 Join our mission, join our team &ndash; and grow with us!
 
 
 
 At Raisin, we care about each other and it is one of our top priorities to foster an open and caring environment in which everyone feels welcome and comfortable. Our culture is strongly driven by our ambitious team, which connects more than 75 different nationalities.
 
 
 As part of our team, you will benefit from:
 
 
 <ul>
 <li>
 Employee Development Budget of &euro;2,000 and four full training days per year.
 </li>
 <li>
 Flexible working hours, home office and 30 vacation days.
 </li>
 <li>
 A company pension scheme (Betriebliche Altersvorsorge), which we support with 20%.
 </li>
 <li>
 Enjoy more than 50+ different sports with Urban Sports Club: We subsidize your membership with more than &euro;20 per month.
 </li>
 <li>
 Do you miss being in the office? The Deutschland Ticket gets you there, which we subsidize with &euro;25 per month.
 </li>
 <li>
 Love cycling? With JobRad, lease the bike of your choice and enjoy tax savings, plus Raisin covers your monthly insurance costs.
 </li>
 <li>
 Hungry all the time? Snacks, daily fresh fruit as well as drinks provided at the office.
 </li>
 <li>
 You are moving from another country or city to join us? We may support your relocation.
 
 
 </li>
 </ul>
 
 Raisin Applicant Privacy Policy
 
 
 
 We value diversity and the unique experiences each individual brings. If you&rsquo;re excited about this role but don&rsquo;t meet every requirement, we still encourage you to apply.
 
 
 We are an equal opportunity employer and are committed to creating an inclusive environment for everyone, regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, or gender identity.
</div>

Team Passerelle

<div>Wir sind das Team Passerelle, eine 2025 in Berlin gegr&uuml;ndete KI-Beratung aus dem gewerkschaftlichen Umfeld. Unser Name ist Programm: Passerelle bedeutet Br&uuml;cke. Wir sind angetreten, um inmitten der gr&ouml;&szlig;ten Transformation der Arbeitswelt genau diese Br&uuml;cke in die Zukunft zu bauen - eine Br&uuml;cke, die echten technologischen Fortschritt erm&ouml;glicht und gleichzeitig bessere Arbeitsbedingungen schafft. All das machen wir unter dem Leitsatz: Gute KI. Gute Arbeit.

Gegr&uuml;ndet von erfahrenen K&ouml;pfen aus der Tech- und Beratungswelt sowie der angewandten KI-Wissenschaft, verbinden wir strategischen C-Level-Weitblick mit tiefem technologischem Sachverstand. Wir begleiten Organisationen und Institutionen auf ihrem Weg in eine KI-gepr&auml;gte Zukunft, von der strategischen Beratung &uuml;ber die werteorientierte technische Implementierung bis zur nachhaltigen Adoption im Betrieb. Neben unseren Transformationsmandaten entwickeln wir au&szlig;erdem eigene Software-Produkte f&uuml;r digitale Souver&auml;nit&auml;t.
<h2 class="jobSectionHeader">
Aufgaben</h2>

Als Data &amp; AI Engineer schaffst du bei uns die zwingende technische Voraussetzung f&uuml;r jeden erfolgreichen KI-Einsatz: eine belastbare und strukturierte Datenbasis. Dein Schwerpunkt liegt darauf, historisch gewachsene, heterogene Datenlandschaften zu erschlie&szlig;en und f&uuml;r moderne KI-Anwendungen, insbesondere Retrieval-Systeme, nutzbar zu machen.

Als eine:r der ersten dedizierten Engineering-Hires gestaltest du den Aufbau unserer technischen Umsetzungskraft mit &ndash; eng an der Seite unseres AI Solutions Architect. Dein T&auml;tigkeitsfeld umfasst zwei Bereiche: In unseren Beratungsmandaten analysierst du die bestehende Datenarchitektur, deckst L&uuml;cken auf und legst das Fundament f&uuml;r die KI-Strategie. Parallel dazu entwickelst du die Daten- und Retrieval-Pipelines f&uuml;r unsere eigene KI-Infrastruktur und Software-Produkte.

** 
Deine Aufgaben**
<ul>
<li>Datenbestandsaufnahme &amp; Reifegrad: Du erstellst Datenlandkarten &uuml;ber heterogene Best&auml;nde hinweg und bewertest den Reifegrad des digitalen Fundaments. Deine L&uuml;ckenanalysen zu Identifikatoren und Metadaten zeigen pr&auml;zise, wo der Hebel liegt.</li>
<li>AI Data Ingestion (KI-Enablement): Du erschlie&szlig;t unstrukturierte Datenquellen (PDFs, Berichte, Publikationen) f&uuml;r die Nutzung in KI-Systemen &ndash; Text-Extraktion, Chunking-Strategien, Metadaten-Generierung, z. B. mit Werkzeugen wie LlamaParse oder Unstructuredio.</li>
<li>Datenfundament &amp; Retrieval: Du entwickelst Metadaten- und Identifikatorkonzepte, Datenmodelle und Embedding-Pipelines und baust die Retrieval-Grundlage f&uuml;r RAG-Anwendungen &ndash; inklusive Bef&uuml;llung und Betrieb von Vektordatenbanken (z. B. Qdrant, Weaviate, pgvector). Die Qualit&auml;t dieser Grundlage bewertest du systematisch.</li>
<li>Datenschutz &amp; Souver&auml;nit&auml;t: Du gehst verantwortungsvoll mit sensiblen Daten um und stimmst dich eng zu AI-Governance-, Datenschutz, EU AI Act und Souver&auml;nit&auml;tsanforderungen ab. Datensparsamkeit und Schutzw&uuml;rdigkeit denkst du von Anfang an mit.</li>
<li>Pipelines f&uuml;r interne Software-Entwicklung: Du baust und betreibst perspektivisch die Ingest- und Retrieval-Pipelines f&uuml;r eigene Software-Produkte &ndash; mit DataOps-Mindset (Versioning, Testing, Observability) und einem Verst&auml;ndnis agentischer Muster inkl. Human-in-the-loop.</li>
</ul><h2 class="jobSectionHeader">Qualifikation</h2>
<ul><li>Fundierte Data-Engineering-Erfahrung: Mehrj&auml;hrige (3+ Jahre) im Data Engineering oder als Data Platform Engineer &ndash; idealerweise in gewachsenen, heterogenen Datenlandschaften. Exzellentes Python und SQL sowie sicherer Umgang mit dem Modern Data Stack (z. B. dbt, Airflow, Dagster) und ETL-/ELT-Prozessen.</li>
<li>KI-Enablement: Praktische Erfahrung mit Embedding-Pipelines und Vektordatenbanken (z. B. Qdrant, Weaviate, Milvus, pgvector), ein Gesp&uuml;r f&uuml;r Retrieval-Strategien und Erfahrung mit der Erschlie&szlig;ung unstrukturierter Daten (z. B. LlamaParse, Unstructuredio).</li>
<li>Datenschutz-Bewusstsein: Erfahrung im verantwortungsvollen Umgang mit sensiblen und personenbezogenen Daten sowie Kenntnis der einschl&auml;gigen Anforderungen (insb. DSGVO, EU-AI-Act-Awareness).</li>
<li>Pragmatismus bei realer Datenlage: Du f&uuml;hlst dich in unvollst&auml;ndigen, gewachsenen Datenbest&auml;nden wohl und wei&szlig;t, dass ein nutzbares Datenmodell mehr wert ist als ein perfektes. Du priorisierst, wo es z&auml;hlt.</li>
<li>Kommunikationsst&auml;rke &amp; Haltung: Du &uuml;bersetzt die Datenrealit&auml;t verst&auml;ndlich f&uuml;r nicht-technische Stakeholder und kommunizierst auf Augenh&ouml;he mit Fachbereichen. Dein Deutsch und Englisch ist verhandlungssicher. Du steuerst dich selbst, denkst l&ouml;sungsorientiert und teilst unsere Werte rund um eine gerechte Arbeitswelt von morgen.</li>
</ul>
Sch&ouml;n, wenn du zus&auml;tzlich mitbringst: Erfahrung mit Knowledge Graphs, Semantic Layer oder Text-to-SQL; ausgepr&auml;gtes DataOps-Mindset; Lust an PoCs mitzubauen
<h2 class="jobSectionHeader">
Benefits</h2>
<ul><li>Echter Impact &amp; Haltung: Ein Arbeitsumfeld, das technologische Innovation mit gesellschaftlicher Verantwortung und nachhaltigen Werten verbindet. Du gestaltest die KI-Transformation an vorderster Front nach europ&auml;ischen, demokratischen Werten.</li>
<li>Sichtbarkeit &amp; Netzwerk: Einblicke in hochkar&auml;tige Mandate aus Politik, Wirtschaft und Gewerkschaften</li>
<li>Hohe Autonomie: Flache Strukturen, die bewusste Abwesenheit von Mikromanagement und echte Verantwortung f&uuml;r deine Accounts und Themen</li>
<li>Flexibles Set-up: Remote-first mit einem Kernteam in Berlin sowie flexiblen Arbeitszeiten, die zu deinem Leben passen.</li>
<li>Standards: 30 Tage Urlaub, ein eigenes Weiterbildungsbudget und modernste Arbeitsausstattung</li>
<li>Faire Verg&uuml;tung: Ein transparentes Gehaltsband von 80.000 bis 95.000 &euro; brutto p.a. auf Basis einer 40h Woche (je nach Erfahrung)</li>
</ul>
<ul><li>Langfristige Perspektive: Die Stelle ist aufgrund unserer agilen Startup-Phase zun&auml;chst auf ein Jahr befristet. Da wir uns im nachhaltigen Aufbau befinden, ist eine langfristige Zusammenarbeit unser klares Ziel. Eine Verl&auml;ngerung oder Entfristung wird bei entsprechender Mandats- und Gesch&auml;ftsentwicklung ausdr&uuml;cklich angestrebt.</li></ul>

Der Kern unserer Arbeit ist die sozialpartnerschaftliche Gestaltung der KI-Transformation - von der fairen Verteilung von Produktivit&auml;tsgewinnen &uuml;ber die digitale Souver&auml;nit&auml;t bis hin zur Frage, wie wir eine gerechte Arbeitswelt von morgen bauen. Wir brennen f&uuml;r diese Themen und wollen diese Br&uuml;cke in die Zukunft nicht nur theoretisch durchdenken, sondern ganz praktisch bauen.

Daf&uuml;r suchen wir kluge K&ouml;pfe mit Haltung und Can-Do-Attitude, die um die Ecke denken und keine Angst haben, Annahmen zu hinterfragen. Bewirb dich ausdr&uuml;cklich auch dann, wenn dein Lebenslauf nicht perfekt auf die Beschreibung passt - f&uuml;r uns z&auml;hlen deine Perspektive und dein innerer Antrieb weit mehr als ein l&uuml;ckenloses Profil. Wenn du Lust hast, ein junges Unternehmen mitzupr&auml;gen und der Zukunft der Arbeit deinen Stempel aufzudr&uuml;cken: Join us!</div>

Data & AI Engineer (m/w/d)

Langdock

<div>
 
 About Langdock
 
 
 
 We help organizations drive internal AI adoption through our enterprise-ready AI platform. Our product suite includes AI Chat, Assistants, Search, API, and Workflows - enabling companies to roll out AI internally and build custom AI use cases. We serve over 7,000 customers, from DAX companies to startups, including enterprise clients like Merck KGaA, Der Spiegel, GetYourGuide, Babbel, and Personio.
 
 
 We are model-agnostic, fully GDPR-compliant, and ISO 27001 &amp; SOC-2 Type 2 certified. Made in Germany, backed by Y Combinator.
 
 
 
 About The Role
 
 
 
 AI is the biggest transformation of our lifetime. Most companies know they need to move. Few know how. This is where you come in.
 
 
 As an
 
 AI Adoption Engineer
 
 in Langdock's Customer Success team, you turn ambition into working solutions. You sit at the intersection of customer, product, and engineering:
 
 discovering high-value use cases, scoping them, building them, and making sure they stick
 
 . You understand a customer's workflows, identify where AI creates real leverage, and deliver solutions they can maintain and scale on their own.
 
 
 This is a
 
 technical role
 
 . You write code. You build MCP servers and custom integrations. You work with agentic coding tools daily. You understand our platform at the code level and you're comfortable operating at the boundary between product and engineering. Everyone at Langdock builds AI agents and workflows to automate their own work. As a AI Adoption Engineer, you set the standard for what that looks like.
 
 
 You work project-based with our most relevant customers: running use case workshops, leading on-site hackathons, building complex agents and integrations, and handing over production-ready solutions. You also serve as the technical expert during Sales conversations, helping prospects understand what's possible and how Langdock fits into their architecture.
 
 
 You work closely with Customer Success to drive adoption, with Product and Engineering to push the platform forward, and with Marketing to create reusable resources that help customers build independently. When you hit a platform limitation, you articulate what needs to change and why, and work with the team to make it happen.
 
 
 
 What You Will Do
 
 
 
 <ul>
 <li>
 Discover and scope high-value use cases: run AI Use Case Workshops, Department Deep-Dives, and Scoping Sessions to identify where AI creates the most impact for each customer.
 </li>
 <li>
 Build and deploy solutions: lead On-Site Hackathons, Implementation Sprints, and Architecture Reviews; deliver working agents, integrations, and workflows that customers can own and maintain.
 </li>
 <li>
 Serve as a technical expert during the Sales process: join sales conversations to answer technical questions, build tailored demos and proof-of-concept environments, evaluate integration architectures, and help prospects understand what Langdock can do for them. You are the person in the room who makes the product feel real for a technical buyer.
 </li>
 <li>
 Drive use case adoption: make sure customers actually adopt what you build; create documentation, run Office Hours, provide hands-on support, and enable power users to build independently.
 </li>
 <li>
 Create reusable resources: build templates, documentation, and reference architectures that Account Management and Marketing can share with customers and prospects.
 </li>
 <li>
 Push the platform forward: identify product limitations blocking high-value use cases, articulate requirements clearly, and work directly with Product and Engineering to close the gaps.
 </li>
 <li>
 Build your own tools: build MCP servers, develop custom integrations, and use agentic coding tools to build on top of the platform and automate your own workflows.
 
 
 </li>
 </ul>
 
 What We're Looking For
 
 
 
 You have
 
 3+ years of experience in a technical, customer-facing role
 
 - Solutions Engineering, Technical Customer Success, Technical Consulting, or similar - where you both built things and worked directly with customers on a real technical product.
 
 
 You have
 
 experience working with enterprise customers on high-impact use cases
 
 : scoping projects, managing stakeholders, and delivering outcomes in large organizations where things move slowly, people change roles, and requirements shift. You've also supported the sales side:
 
 building demos
 
 that close deals, translating a product's capabilities into a prospect's language, and making complex technical products feel tangible.
 
 
 You are
 
 comfortable running workshops
 
 with senior stakeholders and following up with a sharp scoping document that aligns everyone afterwards. You know how to listen for the real problem behind a feature request. You translate complex technical concepts into language non-technical stakeholders understand, and turn customer needs into concrete, buildable specifications.
 
 
 You
 
 understand AI deeply
 
 and know how to turn it into working systems. You can judge when an LLM-based approach is the right tool and when it isn't, and you can hold your own in technical conversations with engineering teams. You
 
 write production-quality code
 
 in JavaScript and Python, and are comfortable with HTML/CSS, APIs, and integration architectures. You can build an MCP server, design a multi-step agent workflow, or debug a customer's integration setup.
 
 Agentic coding tools are a core part of how you operate
 
 .
 
 
 You are fluent in
 
 English (C1+)
 
 .
 
 German is a strong plus
 
 , since many of our customers are German-speaking enterprises.
 
 
 
 Bonus
 
 : you come from a technical consulting background, an AI-native company, or an early-stage startup. Experience with enterprise software, developer tools, or platform products is a plus.
 
 
 
 How We Work
 
 
 
 <ul>
 <li>
 100% on-site in our Berlin office (Greifswalder Strasse 212)
 </li>
 <li>
 Small team, high ownership - we expect everyone to manage themselves, find valuable work, and pull others in when needed
 </li>
 <li>
 We value calm urgency: things break, we stay calm and fix them. No drama, no firefighting culture
 </li>
 <li>
 We value ambitious execution: learning, shipping, and providing value are big drivers of happiness here
 </li>
 <li>
 We value caring ownership: we are kind, we care about our customers and each other, and we have fun at work
 
 
 </li>
 </ul>
 
 What We Offer
 
 
 
 <ul>
 <li>
 Competitive salary with equity participation
 </li>
 <li>
 Free lunch and dinner, every day in the office
 </li>
 <li>
 Premium gym access (right around the corner)
 </li>
 <li>
 Relocation support for those moving to Berlin
 </li>
 <li>
 A front-row seat to the enterprise AI transformation - working with some of the most ambitious companies in Europe
 
 
 </li>
 </ul>
 
 How To Apply
 
 
 
 Apply through our application form. We will ask you a few short questions - take your time with them, they matter more than your CV.
 
 
 Our process: Application - CV Review - Hiring Manager Interview (30 min, video) - Case Study - On-site Day (team meeting, founder interview) - Reference Call - Offer.
</div>

AI Adoption Engineer - m/f/d

REPLY

<div><div>Aufgaben<div><ul><li>
Als Teil unseres innovativen und internationalen Teams &uuml;bernimmst du Verantwortung bei der Realisierung innovativer AI- und GenAI-Anwendungen in unterschiedlichsten Branchen</li><li>
Dies umfasst den eigenverantwortlichen Entwurf, die Entwicklung und Integration GenAI-gest&uuml;tzter, Cloud-nativer Anwendungen unter Einsatz von Amazon Web Services, Microservices auf Basis von Serverless Applications mit AWS Lambda und Amazon API Gateway, Containertechnologien (z.B. Docker, K8s, EKS, Fargate) sowie DevOps-Prinzipien / Infrastructure as Code (AWS CDK, Terraform, CloudFormation), mit denen du Projekte in den reibungslosen Betrieb &uuml;berf&uuml;hrst</li><li>
Du konzipierst und implementierst RAG-Architekturen mit Vektordatenbanken (z.B. Amazon OpenSearch Service, Aurora PostgreSQL mit pgvector), entwickelst Agenten-basierte Systeme und Agent-Orchestrierungen mit Amazon Bedrock und modernen Agent-Frameworks, bindest Tools und Datenquellen &uuml;ber das Model Context Protocol (MCP) an und integrierst Foundation Models sicher und kosteneffizient in die Anwendungslandschaften unserer Kunden</li><li>
Du verantwortest die Anforderungsdefinition mit unseren Kunden und unterst&uuml;tzt aktiv bei der Erstellung von Angeboten</li><li>
Du arbeitest im Rahmen agiler Projekte und entwickelst dich im engen Austausch mit unseren erfahrenen Engineers kontinuierlich weiter</li></ul></div></div><div>
Benefits<ul><div><div><div>
SPORTZUSCHUSS</div></div></div><div><div><div>
MODERNE TECHNOLOGIEN</div></div></div><div><div><div>
HYBRIDES ARBEITEN</div></div></div><div><div><div>
COMPANY EVENTS</div></div></div></ul><div><ul><li>
Bei uns kannst du deine Ideen einbringen und die Weiterentwicklung der Firma mitgestalten</li><li>
Wir bieten dir einen modernen Arbeitsplatz mit super Hardware (nach Wahl) und eine internationale und famili&auml;re Arbeitsumgebung mit flachen Hierarchien. Bei uns l&auml;sst sich der Job gut mit der Familie verbinden</li><li>
Bei uns kannst du ein JobRad bekommen, wir beteiligen uns an deinen sportlichen Aktivit&auml;ten und &uuml;ber die Reply Gruppe bekommst du Zugriff auf zahlreiche weitere Benefits</li><li>
Team Building ist bei uns ein wichtiger Faktor: Im Jahr finden diverse Events statt</li></ul></div></div><div>
Qualifikationen<div><ul><li>
Wir freuen uns auf dein erfolgreich abgeschlossenes Hochschulstudium in Informatik, Ingenieurwesen, Mathematik, Physik oder einem vergleichbaren technischem Studiengang mit erster Berufserfahrung (bis zu 3 Jahre) als AI/ML Engineer</li><li>
Sehr gute Kenntnisse der AWS &mdash; AWS und KI Zertifizierungen (Associate-Level oder h&ouml;her) sind ein Plus</li><li>
Sehr gute Kenntnisse g&auml;ngiger Programmier- und Skriptsprachen (Python, Typescript / JavaScript, Java) und Erfahrung in mehreren Bausteinen des nachfolgenden Tech- und Toolstacks: Node.JS / NPM, Vue.JS, Nuxt.JS, Spring, JUnit, Jest, Mocha, Lodash, Git, Github Actions, Bitbucket Pipelines, AppSync (GraphQL), Amazon API Gateway, AWS Lambda, Infrastructure as Code (AWS CDK, Terraform), sowie SQL und NoSQL Datenbanken (z.B. Amazon RDS, Amazon Aurora, Amazon DynamoDB, Amazon Timestream)</li><li>
Praktische Erfahrung mit Generativer KI und LLMs - insbesondere mit GenAI-Architekturen und ihren Bausteinen wie RAG, Agent-Orchestrierung und Model Context Protocol (MCP) -, AWS AI-Services (z.B. Amazon Bedrock, Amazon SageMaker), Agent-Frameworks (z.B. Strands Agents SDK, LangChain / LangGraph, CrewAI, Vercel AI SDK) sowie mit AI-gest&uuml;tzter und agentischer Softwareentwicklung (z.B. Claude Code, Amazon Q Developer, GitHub Copilot)</li><li>
Erfahrung im Requirements Engineering und in der &Uuml;bersetzung fachlicher Anforderungen in technische L&ouml;sungen</li><li>
Sehr gute Kommunikationsf&auml;higkeit in deutscher und englischer Sprache sowie Bereitschaft zu gelegentlichen Reisen runden dein Profil ab</li></ul></div></div><div>
Develop yourself and the digital future &ndash; at Reply!<div>
Reply ist spezialisiert auf die Konzeption und Implementierung von L&ouml;sungen, die auf neuen Kommunikationskan&auml;len und digitalen Medien basieren. Mit einem Netzwerk hoch spezialisierter Unternehmen definiert und entwickelt Reply Gesch&auml;ftsmodelle, die durch KI, Big Data, Cloud Computing, digitale Medien und dem Internet der Dinge erm&ouml;glicht werden. Reply bietet Beratung, Systemintegration und Digital Services f&uuml;r Unternehmen aus den Bereichen Telekommunikation und Medien, Industrie und Dienstleistungen, Banken und Versicherungen sowie &ouml;ffentliche Verwaltung.</div></div></div>

AI ENGINEER — GENAI & AGENTIC SOLUTIONS (M/W/D) AMAZON WEB SERVICES

Aleph Alpha

<div>
 Our Mission
 
 
 Aleph Alpha is one of the few companies in Europe doing serious foundation model pre- and post-training. We're building models that have general-purpose capabilities, and specifically excel at addressing the needs of our customers.
 
 
 We're looking for exceptional Software Engineers to join our model training team. Most of the team is based in Heidelberg .
 
 
 Team Culture
 
 
 At Aleph Alpha, we foster a culture built on ownership, autonomy, and empowerment. Teams and individual contributors are trusted to take responsibility for their work and drive meaningful impact. We maintain a flat organizational structure with efficient, supportive management that enables quick decision-making, open communication, and a strong sense of shared purpose.
 
 
 We believe a strong engineering culture is the key to model training success. We like Extreme Programming and favor trunk-based development. We often mob-program, which keeps us aligned and means we always learn from each other.
 
 
 
 About The Role
 
 
 
 As a Software Engineer in Model Training, you'll work across our full stack. Some weeks you might be optimizing how training loads are scheduled on our cluster and making the pipeline more robust and performant so we can iterate faster. Other weeks, you'll be enabling large-scale code execution for reinforcement learning. And at other times, you might dig deep into our evaluation codebase to lift inference throughput on evals.
 
 
 No two days are the same. Things move fast, and your ability to focus and prioritize is what lets you unblock the team day-to-day while designing the high-quality tooling and infrastructure that speeds us up long-term.
 
 
 We're still building out our training pipeline and infrastructure. Some pieces exist, some don't, and you'll have real influence on what gets built and how. Your work directly shapes how quickly we can experiment and improve our models.
 
 
 
 Your Responsibilities
 
 
 
 <ul>
 <li>
 Co-own the training pipeline end-to-end. Design, build, and maintain the infrastructure and components that let us iterate fast on experiments.
 </li>
 <li>
 Build high-quality tooling. Model training is a continuous effort, and we deliberately invest in our tooling and infrastructure to stay successful long term.
 </li>
 <li>
 Collaborate across disciplines. We believe in cross-functional teams. Engineers and researchers work closely so we can learn from each other and iterate faster together.
 </li>
 <li>
 Champion good engineering practices. Working incrementally, maintaining fast feedback loops, and refactoring continuously keep a team successful long-term, especially when moving fast.
 </li>
 <li>
 Shape the direction of the team. Our culture empowers individuals to take ownership. If you see that we'll need more GPUs, a different storage system, or a change to how the team is set up, you should drive this change.
 
 
 </li>
 </ul>
 
 Your profile
 
 
 
 In the model training team, we hire slowly and deliberately. We recognise that we need top talent to deliver the best models, and we value ability over experience: if you think you would be a good fit for this role and training an LLM in Europe excites you, we want to hear from you.
 
 
 
 Requirements
 
 
 
 <ul>
 <li>
 A track record of taking initiative to deliver high-impact work.
 </li>
 <li>
 Experience contributing in high-performing teams.
 </li>
 <li>
 Degree in computer science, engineering, or a related field.
 </li>
 <li>
 Willingness to relocate to Germany. Our primary working locations are Heidelberg (preferred) and Berlin, although there is some flexibility to work from other locations in Germany with regular travel to Heidelberg (potentially weekly).
 </li>
 <li>
 Ability to write software that other strong engineers want to read and build on.
 </li>
 <li>
 Desire to take ownership of problems and collaborate with other teams to solve them.
 </li>
 <li>
 Deep interest in how state-of-the-art foundation models work.
 </li>
 <li>
 Strong communication skills, with the ability to convey technical solutions to diverse audiences.
 
 
 </li>
 </ul>
 
 Nice-to-haves
 
 
 
 <ul>
 <li>
 Experience working with distributed systems.
 </li>
 <li>
 Experience working with Kubernetes.
 
 
 </li>
 </ul>
 
 About
 
 
 
 We do not require prior experience in machine learning for this role, but we do value your eagerness to learn. If you have prior experience in ML, we will be particularly excited about:
 
 
 <ul>
 <li>
 Experience bringing AI research innovations into production.
 </li>
 <li>
 Experience in areas such as large-scale data processing or distributed computation for foundation model training or inference.
 </li>
 <li>
 Experience with performance engineering: profiling, benchmarking, and optimizing code for throughput, latency, or memory.
 
 
 </li>
 </ul>
 
 Compensation And Benefits
 
 
 
 <ul>
 <li>
 Become part of an AI revolution!
 </li>
 <li>
 30 days of paid vacation
 </li>
 <li>
 Access to a variety of fitness &amp; wellness offerings via Wellhub
 </li>
 <li>
 Mental health support through nilo.health
 </li>
 <li>
 Substantially subsidized company pension plan for your future security
 </li>
 <li>
 Subsidized Germany-wide transportation ticket
 </li>
 <li>
 Budget for additional technical equipment
 </li>
 <li>
 Flexible working hours for better work-life balance and hybrid working model
 </li>
 <li>
 Virtual Stock Option Plan
 </li>
 <li>
 JobRad&reg; Bike Lease
 </li>
 </ul>
</div>

Senior AI Software Engineer – Model Training (f/m/d)

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AI ENGINEER — GENAI & AGENTIC SOLUTIONS (M/W/D) AMAZON WEB SERVICES

Senior AI Software Engineer – Model Training (f/m/d)