AI Engineer

• Build and scale AI systems: Design, implement, and optimize core components of our agentic AI architecture with a focus on performance and reliability
• Develop agentic workflows: Build on multi-agent systems using LLMs, orchestration frameworks (LangGraph), RAG, and context engineering
• Ship advanced AI features: Develop innovative capabilities such as layout optimization, structured reasoning flows, and multimodal workflows
• Contribute to product evolution: Collaborate closely with product and engineering teams to shape features through experimentation and rapid iteration
• Drive continuous improvement: Help refine best practices for evaluation, deployment, monitoring, and scaling of AI systems in production

Freelance Senior AI Engineer – Multi-agent systems

AI Research Scientist

• Finetune LLMs using RL and other SOTA methods
• Create conversational workflows involving tool-calls and static knowledge
• Work with agent teams to build business logic into conversation
• Employ multimodals to enrich the conversational UX
• Innovate and build novel methods for faster inference

Senior AI Research Scientist

• Neue KI-Technologien bewerten und in praxisnahe Lösungen übersetzen.
• KI-Lösungen konzipieren und umsetzen: Integration von LLMs und KI-APIs, Prompt Engineering, Aufbau von RAG-/Knowledge-Base-Systemen.
• Proof-of-Concepts entwickeln, neue Technologien evaluieren und erfolgreiche Ansätze in einen tragfähigen Zustand bringen – bis hin zur Übergabe an R&D oder in den produktiven Betrieb.
• Daten für KI-Anwendungen aufbereiten (ETL/ELT) und Ergebnisse fortlaufend evaluieren und verbessern.
• Use-Cases für KI im Unternehmen aktiv identifizieren – nicht nur mit der Tech-Brille, sondern mit Blick auf den konkreten Nutzen.
• Ergebnisse präsentieren und Fach- sowie Business-Teams bei KI-Themen beraten – auf dem jeweiligen technischen Niveau deines Gegenübers.

• We are building the newsroom of the future in Germany’s largest financial portal.
• AI handles routine tasks so our editorial team can focus on what truly matters: context, analysis, and point of view.
• AI-driven workflows for market reports, earnings recaps, economic updates, and much more
• Development of multiple n8n flows as a central editorial asset
• Wide selection of tools, API integration, quality assurance – end-to-end content repurposing at scale: articles, newsletters, and social media.

Editorial AI Lead

• You build and evolve MAIA's core product capabilities.
• Your primary focus is backend and AI systems - RAG pipelines, retrieval strategies, agentic workflows, and LLM integrations.
• You also implement the frontend changes needed to ship features end-to-end, while backend remains your main area of work.
• You design, implement, and ship features from discovery to production rollout, with a strong focus on backend and AI functionality.
• You build and iterate on RAG pipelines, retrieval strategies, and LLM integrations, making pragmatic tradeoffs across quality, latency, and cost.
• You build evaluation approaches for LLM features - quality gates, regression detection, and observability that makes non-deterministic systems debuggable.
• You implement frontend changes needed to ship features end-to-end - simple, intuitive UI work that unblocks delivery, typically a small portion of total effort.
• You proactively scout new technologies and approaches, translate findings into production-ready solutions, and push the team's understanding of what is possible.
• You collaborate closely with Product, DevOps, and customer-facing teams to turn requirements into high-quality, maintainable software.

Senior Fullstack Applied AI Engineer

Machine Learning Engineer

• Partner closely with the Operations squads and Data Scientists to accelerate ML and RAG prototypes into resilient, production-ready code.
• Directly integrate with the team to deploy, optimize, and scale heavy-width CV and VLM models focused on fraud detection and luxury product authentication.
• Lead the end-to-end foundational groundwork of our ML lifecycle by designing robust systems for Data & Feature Management, Model Tracking & Registry, and Model Serving & Monitoring.
• Scale infrastructure by automating continuous retraining pipelines that handle diverse deployment cadences (from daily fraud detection to weekly recommendations).
• Design resilient multi-model architectures, and critically evaluate the technical overhead and TCO of our in-house tools against enterprise-grade platforms to ensure long-term resilience.
• Act as a pioneer and cornerstone hire for the ML engineering discipline at Vestiaire Collective, setting the technical standards to help scale the AI/ML organization.
• Transition into a centralized foundational role, moving beyond single-squad operations to mentor the team and provide horizontal ML infrastructure support to multiple domains, including Search, Discovery, Pricing, Marketing, and Data Platforms.

• owning the transition from research code to production-ready and optimized models
• establishing CI/CD pipelines that allow scientists to deploy models in short iteration cycles
• innovating upon our existing monitoring systems to make our services reliable and give scientists insight into performance of models in production
• designing services to expose ML models to Zillow’s end customers
• owning team’s datasets, leading and supporting data engineering projects, and collaborating with scientists on model training
• supporting scientists in running large-scale training and data processing projects
• establishing best practices around code quality, testing, and ownership for reliability
• participating in existing on-call rotation
• staying updated on cutting-edge research and modifying methods for practical use
• collaborating across applied science and engineering teams to turn ideas into scalable product capabilities

• Build and grow a high-performing ML team
• Align technical decisions across ML, product, and engineering
• Drive hands-on problem solving across our large-scale production system — getting models to work, shipping them, and iterating fast
• Own the full model development lifecycle, from research and prototyping through deployment, monitoring, and maintenance
• Architect a scalable ML platform that enables rapid experimentation and reliable production releases
• Champion the development of new analytics products within the team
• Design and implement geospatial analytics using state-of-the-art deep learning techniques and statistics
• Establish clear decision criteria for model development, ensuring alignment across the team and with product requirements

Team Lead, Machine Learning

• You’ll join the Agentic Ants team as our second native AI engineer.
• You’ll build and extend our agentic systems alongside the rest of the team.
• You’ll co-own the evaluation framework.
• Work across both Synera MAS and Synera Assistant.
• Partner with the wider engineering team to help go deeper on customer insights data.
• The evaluation framework is live with golden datasets across multiple agent surfaces.
• Co-own at least one agent surface with the team and become the go-to person for evaluation methodology at Synera.

• Develop tailored AI applications with a focus on generative AI.
• Evaluate the quality of models with respect to correctness, robustness and fairness.
• Ensure compliance with ethical and regulatory requirements.
• Collaborate with developers and quality engineers.
• Implement RAG applications and leverage agentic frameworks for innovative solutions.
• Support DevOps teams with best practices in LLMOps and security concepts.
• Select and evaluate off-the-shelf AI systems, taking cost, latency and scalability into account.
• Generate datasets for evaluation and implement industry standards in the field of AI.
• Conduct market research on new AI solutions to assess their potential for applications.

Senior AI Engineer

• Design and develop next-generation architectures at the interface of multimodal perception, foundation models, behavior learning, and end-to-end autonomous driving.
• Co-design innovative software that transitions state-of-the-art research into production-ready systems.
• Execute training and evaluation cycles on large-scale datasets, from data curation and representation learning to failure diagnosis and model optimization.
• Integrate backbone and foundation models into BMW’s autonomous driving perception and planning stack to ensure robustness across different markets and Operational Design Domains (ODDs).
• Evaluate and advance current methods in self-supervised learning, multimodal fusion, and end-to-end architectures for real-world deployment.
• Contribute across the full development lifecycle, from early research to series production release, with direct impact on customers worldwide.

Senior ML Engineer – Next-Generation Autonomous Driving

• Develop and optimize machine learning models with a focus on computer vision (e.g., image recognition, object detection) and NLP (e.g., text classification, sentiment analysis, entity recognition).
• Train and fine-tune deep learning models (e.g., CNNs, RNNs, Transformers) for tasks such as reverse image search, image-text matching, and online content analysis.
• Collaborate with cross-functional teams to improve product and system performance.
• Research and implement state-of-the-art algorithms and techniques to improve the accuracy and performance of AI solutions.
• Build scalable data pipelines for the ingestion and processing of image and text data for model training and evaluation.
• Continuously monitor the performance of models in production, identify bottlenecks, and propose optimizations.

• Driving the digital development of production sites in Germany toward AI-supported Smart Factories
• Establishing efficient AI implementation and operating models, from structured solution development through stable operations to continuous improvement in day-to-day business
• Applying explainable analysis and modeling approaches (Explainable AI) to make AI-supported decisions transparent, understandable, and actionable for people
• Continuously monitoring new technologies, methods, and trends, critically assessing them, and selectively transferring suitable approaches into practical, industrial solutions for our production sites
• Closely collaborating with national and international teams, promoting knowledge transfer, and supporting cross-site harmonization and sustainable scaling of successful solutions

AI Engineer – Smart Factories

• Development of Pruna’s SaaS Platform
• Contribute to the development and maintenance of Pruna’s SaaS cloud platform, designed for model compression and benchmarking
• Design and develop responsive, user-friendly interfaces using basic frontend technologies
• Implement and optimize backend services to support the platform’s functionalities, including model compression, performance evaluation, and user interaction through the UI
• Collaborate with software engineers, ML engineers, and product teams to ensure seamless integration and high-quality user experience
• Oversee the R&D cluster of the company, including individual machines on AWS that users can start and work on daily
• Manage and maintain cluster for ML workload, facilitating the execution of batched experiments and jobs for the team
• Ensure optimal performance, scalability, and reliability of the compute resources used for research and development
• Scale ML Research Engineers' work, manage experimentation setups, pipelines, and testing environments
• Collaborate with MLOps Engineers on the integration of Pruna's solutions in proprietary and established cloud platforms
• Qualify new partnerships, experiment together on new tools and offering for the ML community to generate leads and convert customers

MLOps Engineer

• Design and develop secure AI applications that deliver real value to users
• Use LLMs and Azure AI Foundry to build prototypes and production services
• Extend Microsoft Copilot using Copilot Studio
• Develop custom agents and scale solutions broadly via the Power Platform and Power BI
• Work closely with colleagues to make data and analytics infrastructures (Power BI & Fabric) AI-ready
• Integrate AI solutions into Dynamics 365 Business Central and other applications within the Microsoft 365 ecosystem
• Implement security and compliance standards and document best practices for sustainable use

AI Developer

• Advising our clients on the use of AI to increase employee productivity and efficiency (from non-technical staff to development teams) and on process improvements
• Conducting workshops and one-on-one sessions on all things AI with our clients
• Analyzing and identifying AI use cases within client environments
• Assessing current AI technologies and platforms, reviewing solution concepts, and applying best practices to client situations
• Enabling significant efficiency gains for our clients through AI adoption
• Independently developing GenAI platforms
• Designing and building AI and chatbot solutions
• Developing and implementing AI-driven solutions to automate complex processes

Cloud & AI Engineer

• Als AI Engineer (w/d/m) schreibst du nicht nur Notebooks und Prompts, du baust produktive GenAI-Systeme: Agenten, RAG-Pipelines, LLM-getriebene Workflows.
• Du arbeitest in crossfunktionalen, agilen Teams an Kundenprojekten und kommst dabei mit dem gesamten modernen GenAI-Stack in Berührung.
• Zusammen mit deinem Team berätst du unsere Kunden bei Use-Case-Identifikation,
• Architektur-Entscheidungen, Aufwandsschätzungen und der Auswahl geeigneter Vorgehensmodelle.
• Du analysierst Kundenanforderungen, entwirfst und implementierst agentenbasierte Lösungen und sorgst auch für gute Dokumentation.
• Du behältst stets die Qualität auf dem Schirm — und das heißt bei AI: systematische Evals, Regression-Tests gegen Goldsets, Hallucination-Monitoring, Cost-per-Inference-Tracking, Prompt-Versionierung, Observability für Agent-Traces.
• Du nutzt moderne, automatisierte Ansätze und AI-native Entwickler-Workflows zur Optimierung von Code, Fehlererkennung und für innovative Lösungen.

AI Engineer, Agentic AI Engineer

• Join a team of 35 tech sassy Internet of Things and Sensor Technology engineers.
• Partnering with your team, you: run Machine Learning and AI models directly on local IoT sensors (rather than on cloud servers).
• Design, build, and maintain scalable infrastructure for AI-driven applications and bring model inference onto drone detection devices.
• Implement scalable, low-latency intelligence at the edge.
• Own and optimize CI/CD pipelines to enable fast and secure deployments.
• Ensure system reliability, performance, security, low-latency predictions and offline operation in sensitive, mission-critical environments.
• Work closely with ML engineers to deploy and scale models in production.
• Automate infrastructure using Infrastructure as Code (e.g., Terraform).
• Monitor systems, troubleshoot incidents, and continuously improve observability.
• (if you want) support field operations by acquiring sensor data on site in Germany, Poland or Hungary and validating the technology through controlled field trials.

Senior Edge ML Engineer – Linux

• KI-Standard mitgestalten: Du wirkst bei der Weiterentwicklung unseres Prompt- und Konfigurationsstandards mit, hilfst klar abzugrenzen, was in den Standard gehört und was kundenspezifisch konfiguriert wird, und entwickelst Sicherheitsnetze, damit die KI in den allermeisten Fällen sicher und zielgerichtet agiert
• Wiederverwendbare Bausteine: Du hilfst, robuste Prompt-Patterns und Konfigurations-Module aufzubauen und bereitest Guidelines sowie Best Practices so auf, dass Team und Service-Partner sie leicht anwenden können
• 2nd-Level-Support: Du unterstützt das Customer-Team bei kniffligen Fällen, bereitest fundierte Empfehlungen vor und übernimmst bei Bedarf die Abstimmung mit Kundinnen und Kunden
• Onboardings begleiten: Du unterstützt bei Kunden-Onboardings, nimmst Konfigurationen vor und beobachtest die Wirkung im Live-Betrieb, um zusammen mit Produkt und Engineering gezielte Verbesserungen abzuleiten
• CARL weiterbringen: Du arbeitest an der Weiterentwicklung unseres KI-basierten Onboarding- und Supportagenten CARL mit (Prompting, Tools, Workflows) und trägst dazu bei, Konfigurationsanpassungen für uns und Partner effizienter zu machen
• Evaluation und Monitoring: Du unterstützt beim Aufsetzen von Metriken, Tests und A/B-Experimenten für KI-Dialoge (z. B. Task Success, Sicherheit, Halluzinationen), richtest Monitoring mit ein und lieferst strukturierte Analysen für iterative Verbesserungen

AI Engineer – Healthcare

• Own the LLMOps pipeline: Evaluate infrastructure, prompt optimization loop, and the production integration that turns experiments into reliable customer-facing features
• Design evaluation strategy per output type: Decide when to use deterministic evals (exact match, schema validation, embeddings) vs. LLM-as-judge, and build the rubrics, test datasets, and human-review loops that make the system trustworthy
• Drive prompt engineering and optimization across all LLM operations in the product: Moving from hand-tuned prompts to a measurable, iterative process
• Pick the right tool for each problem: Some things are LLM problems, some are embedding + classical NLP problems, some are deterministic logic
• Run the production side of AI features: Observability (Langfuse /LangSmith / similar), cost and latency engineering, incident response when an LLM feature degrades
• Build human-in-the-loop workflows: Review queues, feedback ingestion, labeling; so production signal feeds back into evals and prompt iteration
• Mentor our AI & Analytics Intern and contribute to how we build the AI team over time

See all jobs on JobTailor

Freelance Senior AI Engineer – Multi-agent systems

Senior AI Research Scientist

AI Engineer

Editorial AI Lead

Senior Fullstack Applied AI Engineer

Machine Learning Engineer

Machine Learning Engineer

Team Lead, Machine Learning

AI Engineer

Senior AI Engineer

Senior ML Engineer – Next-Generation Autonomous Driving

Machine Learning Engineer

AI Engineer – Smart Factories

MLOps Engineer

AI Developer

Cloud & AI Engineer

AI Engineer, Agentic AI Engineer

Senior Edge ML Engineer – Linux

AI Engineer – Healthcare

AI Engineer – LLM Ops & Evaluation