Stellenbeschreibung:

Senior AI Engineer

Position Overview

We are seeking a Senior AI Engineer to be the technical architect and leader of our AI-powered partnership intelligence platform. This role combines cutting-edge AI engineering with deep collaboration across our specialized team of Full Stack Data Engineer, Full Stack Backend Engineer, and Senior QA Engineer. You will be responsible for designing, building, and scaling the most advanced AI systems in the partnership space, working hand-in-hand with our engineering team to deliver unprecedented partnership success rates through intelligent automation.
Role & Responsibilities

AI Architecture & Technical Leadership

• Cognitive Partner Engine Leadership: Architect and lead the development of our flagship AI system, designing sophisticated LLM-powered intelligence that processes millions of partnership data points to deliver actionable insights with unprecedented accuracy
• Production AI System Design: Build enterprise-grade AI infrastructure capable of handling real-time partnership analysis, multi-modal data processing, and complex reasoning workflows using state-of-the-art transformer architectures
• AI Team Technical Vision: Define and drive the technical roadmap for AI capabilities, working closely with our Full Stack Data Engineer to ensure seamless data flow and with our Backend Engineer for robust API integration
• Advanced Model Architecture: Design and implement custom transformer models, fine-tuning strategies, and ensemble approaches optimized for partnership intelligence and business relationship analysis
Large Language Model Excellence

• Production LLM Development: Lead end-to-end LLM implementation from research to production, including fine-tuning foundation models (GPT-4, Claude, Llama) for partnership-specific use cases and domain adaptation
• Advanced RAG Systems: Build sophisticated Retrieval-Augmented Generation systems that combine proprietary partnership data with LLM reasoning to deliver contextualized, accurate partnership recommendations
• Prompt Engineering & Optimization: Develop production-grade prompt engineering frameworks with versioning, A/B testing, and continuous optimization to ensure consistent, reliable AI outputs
• LLM Safety & Guardrails: Implement comprehensive AI safety measures including bias detection, content filtering, hallucination prevention, and ethical AI deployment practices
AI-Data Integration & Collaboration

• AI-Data Pipeline Synergy: Work intimately with our Full Stack Data Engineer to design data architectures that optimize AI model training, inference, and continuous learning from partnership interactions
• Real-time AI Processing: Build systems that process streaming partnership data, email communications, calendar interactions, and CRM updates to provide instant AI-powered insights and recommendations
• Feature Engineering Leadership: Collaborate on advanced feature engineering pipelines that transform raw partnership data into AI-ready formats, ensuring optimal model performance and accuracy
• Model Training Infrastructure: Design and implement distributed training systems on AWS that can handle large-scale model training, fine-tuning, and continuous learning workflows
Production AI Deployment & Optimization

• Scalable AI Inference: Build high-performance, low-latency AI inference systems capable of handling thousands of concurrent partnership analysis requests with sub-second response times
• Model Performance Optimization: Implement advanced optimization techniques including quantization, pruning, distillation, and efficient serving architectures to maximize performance while minimizing costs
• AI System Monitoring: Collaborate with our QA Engineer to establish comprehensive AI system monitoring, including model drift detection, performance degradation alerts, and quality assurance for AI outputs
• Continuous Model Improvement: Design automated systems for model evaluation, A/B testing, and continuous improvement based on real-world partnership outcomes and user feedback
Cross-functional AI Leadership

• Engineering Team Collaboration: Lead AI integration across our entire tech stack, ensuring our Backend Engineer can seamlessly expose AI capabilities through APIs and our QA Engineer can effectively test AI system reliability
• AI Strategy & Innovation: Drive AI research and development initiatives, staying at the forefront of LLM advances and implementing cutting-edge techniques in production partnership intelligence systems
• Technical Mentorship: Provide AI expertise and guidance to the entire engineering team, fostering AI literacy and ensuring best practices in AI development across all systems
• Product AI Integration: Partner with product and business stakeholders to translate partnership intelligence requirements into advanced AI capabilities that deliver measurable business value
Required Qualifications

Advanced AI Engineering Expertise

• 7+ years of production software development experience with 4+ years specializing in AI/ML systems and 2+ years with production LLM deployment
• Expert LLM Development: Deep hands-on experience with transformer architectures, fine-tuning techniques (LoRA, QLoRA, full fine-tuning), and production deployment of models like GPT-4, Claude, Llama, or similar
• Advanced RAG & Semantic Search: Proven expertise building production RAG systems, vector databases, embedding models, and semantic search capabilities for complex business applications
• Production AI Architecture: Extensive experience designing and implementing scalable AI systems that handle real-time inference, model serving, and continuous learning in enterprise environments
Deep Technical Foundations

• Programming Excellence: Expert-level Python programming with deep knowledge of ML frameworks (PyTorch, TensorFlow), AI libraries (Hugging Face, LangChain), and production software engineering practices
• Machine Learning Mastery: Advanced understanding of ML algorithms, neural network architectures, optimization techniques, and mathematical foundations including linear algebra, calculus, and statistics
• Cloud AI Infrastructure: Extensive AWS experience including SageMaker, Bedrock, Lambda, ECS for AI workloads, with hands-on knowledge of distributed training and model deployment at scale
• Data Integration Skills: Strong experience working with data pipelines, ETL processes, and database systems to feed AI models with high-quality, real-time data
AI Safety & Production Excellence

• AI Ethics & Safety: Deep knowledge of responsible AI development, bias detection and mitigation, AI safety practices, and implementing guardrails for production AI systems
• Model Optimization: Advanced skills in model compression, quantization, pruning, and inference optimization for deploying efficient, cost-effective AI systems
• Performance Monitoring: Experience with MLOps practices, model monitoring, drift detection, and maintaining AI system performance in production environments
• A/B Testing & Evaluation: Hands-on experience with AI system evaluation, A/B testing frameworks, and measuring real-world AI impact on business metrics
Leadership & Collaboration Skills

• Technical Leadership: Proven experience leading AI initiatives, mentoring engineers, and driving technical decisions in fast-paced startup environments
• Cross-functional Partnership: Exceptional collaboration skills for working with data engineers, backend engineers, QA engineers, and product teams to deliver integrated AI solutions
• Communication Excellence: Outstanding ability to explain complex AI concepts to technical and non-technical stakeholders, translate business requirements into AI capabilities
• Innovation Drive: Track record of implementing state-of-the-art AI research in production, staying current with latest developments, and driving innovation in AI applications
Preferred Qualifications

Advanced AI Specialization

• Ph.D./Master's in AI/ML: Advanced degree in Computer Science, Machine Learning, AI, or related field with focus on practical AI system development
• Multi-modal AI Systems: Experience building AI systems that process text, structured data, and other modalities simultaneously for comprehensive business intelligence
• Reinforcement Learning: Knowledge of RLHF, reinforcement learning techniques, and training AI systems to optimize for business outcomes rather than just accuracy
• AI Research Background: Experience with AI research, publications, open-source contributions, or implementing cutting-edge research in production systems
Domain & Industry Expertise

• Partnership Intelligence: Understanding of partnership management, business relationship dynamics, and experience building AI systems for B2B or relationship-focused applications
• Enterprise AI Deployment: Experience with enterprise-scale AI deployments, handling complex integration requirements, security concerns, and regulatory compliance
• Startup AI Leadership: Experience building AI systems from scratch in startup environments, balancing innovation with practical business constraints and rapid iteration
Advanced Technical Skills

• Distributed AI Systems: Experience with distributed training, model parallelism, and building AI systems that scale across multiple GPUs and cloud regions
• Custom Model Development: Experience developing custom AI models beyond fine-tuning, including novel architectures, training approaches, and domain-specific innovations
• AI Infrastructure Optimization: Deep knowledge of AI infrastructure optimization, cost management, and building efficient AI systems that deliver maximum ROI
Technical Environment

Core AI Stack

• LLM Frameworks: Advanced work with GPT-4, Claude, Llama, Mistral, and other state-of-the-art language models
• ML Frameworks: PyTorch (primary), TensorFlow, JAX for model development and training
• AI Libraries: Hugging Face Transformers, LangChain, LlamaIndex for LLM integration and deployment
• Vector Systems: Pinecone, Weaviate, or Chroma for semantic search and RAG implementations
Production AI Infrastructure

• AWS AI Services: SageMaker, Bedrock, Lambda, ECS, EC2 for scalable AI deployment
• Model Serving: Custom inference servers, batching systems, and low-latency API endpoints
• Monitoring: Comprehensive AI system monitoring, drift detection, and performance analytics
• MLOps: Model versioning, experiment tracking, automated retraining, and deployment pipelines
Data Integration & Collaboration


• Data Pipeline Integration: Seamless connection with our Full Stack Data Engineer's ETL systems and real-time data streams
• API Integration: Close collaboration with Backend Engineer for exposing AI capabilities through robust, scalable APIs
• Quality Assurance: Partnership with QA Engineer for comprehensive AI system testing, validation, and reliability assurance
• Analytics Integration: Connection with PostHog, Mixpanel, Grafana for AI performance and business impact monitoring
Team Collaboration Structure

AI-Data Engineering Partnership

• Daily technical alignment with Full Stack Data Engineer on data pipeline optimization, feature engineering, and real-time data processing for AI models
• Joint architecture decisions on data schemas, storage optimization, and analytics integration that support both AI inference and business intelligence
• Collaborative model training including data quality assurance, feature validation, and continuous learning system design
AI-Backend Integration

• Seamless API development with Full Stack Backend Engineer to expose AI capabilities through Next.js/NestJS applications
• Performance optimization collaboration ensuring AI inference integrates efficiently with application workflows and third-party integrations
• Joint system architecture for handling AI workloads alongside traditional backend processing and database operations
AI-QA Excellence

• Comprehensive AI testing strategy developed with Senior QA Engineer, including model output validation, bias testing, and system reliability assurance
• Joint quality frameworks for AI-specific testing approaches, automated validation, and continuous monitoring of AI system performance
• Collaborative debugging and optimization of AI systems based on quality metrics and production performance analysis

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