Role Description NeuraPlox sucht den Engineer, der unsere AI-Agent-Plattform für Enterprise-Kunden in regulierten Branchen baut. Kein Tutorial-LLM, kein Vibe-Engineering, keine Demo-RAGs, die bei 10k Embeddings auseinanderfallen. Wir bauen explainable AI mit Audit-Lineage, Decision-Provenance und pgvector at scale, weil unsere Kunden ihre KI-Outputs nicht nur erklären wollen, sondern müssen. Aufgaben - Production-grade AI-Agent-Plattform mit PydanticAI, LiteLLM, Langfuse - RAG-Pipelines mit pgvector at scale (100M+ Embeddings, nicht "wir testen mal mit 1000") - Multi-Tenant Token-Quota-Service plus Eval-Frameworks pro Tenant - MCP (Model Context Protocol) Tool-Integration als First-Class-Citizen - Explainable Outputs, Audit-Lineage, Decision-Provenance für regulierte Branchen - Architektur-Entscheidungen, die im Bank-, Versicherungs- und Pharma-Kontext halten Wie wir arbeiten - Eval-driven, nicht Vibe-driven. Jeder Agent-Change geht durch eine Eval-Suite. - ADRs für jede relevante Architektur-Entscheidung, nicht im Slack-Thread vergraben - LLM-Calls werden gemessen, nicht geraten - Langfuse von Tag eins - Re-Ranking, pgvector-Sizing und Retrieval-Quality werden empirisch entschieden - Direkter Draht zum Founding-Team, kein Layer aus Project Managern dazwischen Qualifications - 5+ Jahre Python-Production-Engineering - Echte Production-LLM-Erfahrung mit Tokens, Latenz, Cost und Eval als täglicher Realität - RAG-Engineering mit pgvector-Sizing und Re-Ranking, nicht nur "ich kenne LangChain" - LangGraph, PydanticAI oder vergleichbares Agent-Framework in Produktion eingesetzt - LiteLLM, Langfuse, MCP - oder Bereitschaft, dich in 2 Wochen einzuarbeiten Requirements - Tech-Stack: Python, FastAPI, PydanticAI, LangGraph, LiteLLM, Langfuse, pgvector, MCP, Kubernetes-Grundlagen. Benefits - VSOP als Investor-backed Startup für Schlüsselpositionen - Voll-remote möglich, Wohnsitz Deutschland - Team-Entwicklungstage ca. 1xMonat im Munich-Office - Du reportest direkt an Christoph Knöll (Founder/CEO) und arbeitest eng mit unserem Head of AI - Senior-Hire für das AI-Layer, du prägst die Agent-Architektur, nicht "tunest Prompts" - CV plus 3 Sätze, warum genau diese Rolle. Kein Anschreiben-Template. - Wir lesen jede Bewerbung selbst. Company Description Neurawork ist ein junges KI-Beratungsunternehmen mit Schwerpunkt auf Künstliche Intelligenz. Wir bieten KI-Upskilling, KI-Strategieentwicklung und Beratung für Selbstständige und Unternehmen an. Sei von Anfang an dabei, wenn wir großes Schaffen!

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we're helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary AI Engineer Mastercard's Business & Market Insights (B&MI) group empowers organizations to achieve growth and innovation goals by delivering unparalleled data-driven intelligence and cutting-edge AI solutions. By harnessing proprietary data, frontier generative AI, and global expertise, B&MI helps businesses make smarter, faster, and more impactful decisions. We transform complex, multi-modal data into agentic systems and generative applications that drive measurable business outcomes and sustained competitive advantage. We are looking for a Lead Engineer, Generative AI & ML Engineering for the Operational Intelligence Program within B&MI. This role will lead a team of Gen AI engineers to architect and deliver next-generation LLM, agentic, and multimodal AI systems that enable business growth, elevate customer experience, and ensure secure, scalable, production-grade AI. As a technical leader, you will set the engineering standard for Gen AI development - driving innovation across agentic orchestration, retrieval-augmented generation, LLMOps, and responsible AI - while fostering a culture of continuous learning and engineering excellence. Roles & Responsibilities• Architect and lead the development of multi-agent AI systems using frameworks such as LangGraph, CrewAI, and AutoGen - enabling autonomous reasoning, tool use, inter-agent coordination, and adaptive decision-making at enterprise scale.• Design and operationalize multimodal generative AI pipelines that unify text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, GPT-4o, Gemini) for rich, cross-modal intelligence.• Build production-grade RAG and Graph-RAG systems integrating vector databases (Pinecone, pgvector, OpenSearch) and knowledge graphs (Neo4j, AWS Neptune) for semantic retrieval, entity-aware reasoning, and grounded generation.• Lead LLM fine-tuning, prompt engineering, and model alignment strategies - including RLHF, PEFT, LoRA, and instruction tuning - to adapt foundation models for specialized enterprise use cases.• Establish robust LLMOps and MLOps pipelines on Databricks (AWS) using MLflow, feature stores, prompt evaluation frameworks, model lineage tracking, and continuous retraining workflows to ensure reliable AI delivery.• Develop high-performance Python backend services for LLM inference orchestration, async job handling, streaming responses, and distributed data workflows supporting high-throughput Gen AI operations.• Engineer state, memory, and context management subsystems that enable agents to reason temporally, maintain session continuity, manage long-context windows, and coordinate across tools and modalities.• Implement Responsible AI and AI governance practices - including bias detection, hallucination mitigation, explainability dashboards, output safety guardrails, and compliance with data ethics standards - ensuring transparency and fairness of deployed models.• Apply traditional ML and statistical modeling (regression, clustering, forecasting, ensemble methods) in hybrid architectures alongside LLMs for interpretable, explainability-first decision systems.• Continuously research, evaluate, and productionize advancements in generative modeling, agentic AI, multimodal transformers, and frontier foundation models - benchmarking against enterprise-scale performance and safety requirements. All About You• Master's or Bachelor's degree in Computer Science, AI/ML, or Engineering, with significant hands-on experience leading and delivering complex Gen AI or ML engineering programs in production environments.• Expert-level, hands-on experience designing, building, and deploying large language model (LLM) applications, agentic systems, and RAG pipelines - from prototype to production.• Deep proficiency with LLM ecosystems: OpenAI, Anthropic, Gemini, Hugging Face, LangChain/LangGraph, and open-source foundation models (LLaMA, Mistral, Falcon, etc.).• Strong command of Gen AI engineering patterns: prompt engineering, chain-of-thought reasoning, tool/function calling, vector embeddings, semantic search, and agent memory architectures.• Solid applied knowledge of ML fundamentals - predictive modeling, deep learning (PyTorch, TensorFlow), and statistical techniques - used in tandem with Gen AI for hybrid, interpretable systems.• Excellent Python engineering skills including async programming, API development (FastAPI), and building inference-ready microservices; SQL proficiency required.• Hands-on experience with cloud AI infrastructure (AWS SageMaker, Bedrock, Azure OpenAI, or GCP Vertex AI) and familiarity with MLOps/LLMOps tooling (MLflow, Weights & Biases, etc.). Strong analytical, communication, and stakeholder management skills - with the ability to translate complex Gen AI concepts into business value and lead cross-functional teams toward delivery. Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable_accommodation@mastercard.com and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly. Corporate Security Responsibility All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must: - Abide by Mastercard's security policies and practices; - Ensure the confidentiality and integrity of the information being accessed; - Report any suspected information security violation or breach, and - Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines. In line with Mastercard's total compensation philosophy and assuming that the job will be performed in the US, the successful candidate will be offered a competitive base salary and may be eligible for an annual bonus or commissions depending on the role. The base salary offered may vary depending on multiple factors, including but not limited to location, job-related knowledge, skills, and experience. Mastercard benefits for full time (and certain part time) employees generally include: insurance (including medical, prescription drug, dental, vision, disability, life insurance); flexible spending account and health savings account; paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave); 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire; 10 annual paid U.S. observed holidays; 401k with a best-in-class company match; deferred compensation for eligible roles; fitness reimbursement or on-site fitness facilities; eligibility for tuition reimbursement; and many more. Mastercard benefits for interns generally include: 56 hours of Paid Sick and Safe Time; jury duty leave; and on-site fitness facilities in some locations. Pay Ranges Remote - St. Louis: $140,000 - $231,000 USD Job Posting Window Posting windows may change based on the volume of applications received and business necessity. Candidates are encouraged to apply expeditiously.

Role Description Tiger Tracks is hiring our first AI Engineer. This is not a traditional engineering role. We're looking for a top-tier AI-native builder who can rapidly ship production-ready tools using platforms like Lovable, Cursor, Replit, or Bolt. Someone who can turn ideas into working products in 24 to 48 hours, not weeks. You'll work directly with our co-founder Cliff Simmons (ex-Google) to build proprietary AI tools used internally and deployed across client accounts. This is a contract-to-hire role with a path to becoming Head of AI. What You'll Build - Landing page conversion analyzers with auto-fix capability - Creative analyzers for video ads - Vertical media buying co-pilots for Meta, Google, and TikTok APIs - Automated BD audit tool for prospect ad accounts - 20+ more ideas waiting for someone who can ship them We don't care which LLM sits underneath (Anthropic, Gemini, OpenAI, whatever ships best). The value is in the vertical specialization on top. Qualifications - 4+ years of full-stack development experience, including at least 1 year shipping LLM-powered features in production - Strong backend (Python, Node) and frontend (React) skills - Proven track record integrating complex external APIs - Top 1% with Lovable, Replit, Cursor, or Bolt - Public GitHub or portfolio with shipped projects we can play with - Comfortable moving from idea to working prototype in 24-48 hours Requirements - Thrive in greenfield environments - Shipping mindset: rapid prototyping over architecture debates - Genuinely excited about MarTech, AdTech, or performance marketing Structure - Hourly contract, billed bi-weekly net-14 - Monthly renewal based on output and mutual fit - Path to FTE and Head of AI for the right person - Remote, US timezones What We're Not Looking For - Engineers whose AI experience is mostly demos and tutorials - Generalists who'd rather think about architecture than ship code - Anyone who needs a PM, a designer, and a 6-week sprint to deliver a v1 - Big-tech engineers looking for a calmer second act. This will be more intense, not less. How to Apply Send a short note, your GitHub or portfolio link, and one project you're proud of (with the live link if possible) to careers@tigertracks.ai . Tell us what you'd build first if we handed you Lovable, an OpenAI API key, and a Meta Ads account on day one.

• Diseñar y construir sistemas de IA en producción que resuelven problemas reales de negocio, end-to-end: desde discovery hasta operación. • Trabajar con producto y operaciones para traducir problemas ambiguos en soluciones acotadas, medibles y mantenibles. • Construir pipelines de datos que alimentan tanto modelos como superficies de producto. • Diseñar sistemas de evaluación rigurosos: cómo medimos calidad, cómo detectamos regresiones, cómo decidimos cuándo algo está listo para shippear. • Integrar LLMs en producción con todos los retos que eso implica: prompt engineering serio, tool use, manejo de costos y latencia, control de calidad de outputs. • Tomar decisiones técnicas sobre qué tipo de solución aplicar a cada problema. No todo necesita un LLM, no todo necesita ML, y parte del rol es saber distinguir. • Diseñar e implementar las salvaguardas de seguridad del sistema: límites de autonomía, aprobaciones humanas, aislamiento entre clientes, auditoría. • Colaborar con el equipo de backend para definir contratos e integraciones limpias entre servicios. • Mentorear a otros ingenieros en prácticas de IA aplicada conforme el equipo crezca (responsabilidad creciente con el nivel de seniority).