Generative AI Engineers in Germany

Manufacturing intelligence platform with enterprise workflows, RAG, real-time AI assistant features, and multi-repository backend architecture.

• Built and extended production AI/backend services with Django, DRF, FastAPI, GraphQL, Celery, PostgreSQL, MySQL, Redis, and WebSockets across a modular multi-repository platform.
• Contributed to ARIS V2, a real-time manufacturing AI assistant using LangChain, LangGraph, MCP tool orchestration, planning/execution flows, OpenAI, AWS Bedrock, Qdrant, and Elasticsearch/OpenSearch-backed retrieval.
• Supported rollout expansion from ARIS V1 in 4 of 17 client production plants to ARIS V2 currently active in 13 of 17 plants, increasing real-world deployment coverage to more than 50% of the client footprint.
• Worked on document-grounded RAG functionality including ingestion, OCR, chunking, embeddings, indexing, retrieval, reranking, and grounded answer generation for industrial workflows.

Stack: Python, Django, DRF, FastAPI, LangChain, LangGraph, GraphQL, Celery, WebSockets, OpenAI, AWS Bedrock, Qdrant, Elasticsearch/OpenSearch, PostgreSQL, MySQL, Redis, Docker.

Development of an AI console for integration into different open source solutions (ERP, CRM..)

Development of the target architecture Integration of different AI platforms (ChatGPT, Anthropic, Perplexity) Workflow with cross-platform use of the AI platforms Voice input and voice output History Console-based project management Generation of custom agents (Crewai..) Integration of the agents into the AI workflow

• Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
• Used multiple experts (OpenAI models) collaborating during document drafting
• Extracted useful information from the knowledge graph
• Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
• Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
• Deployed initial application as a Streamlit app

• Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
• Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
• Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
• Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
• Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
• Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
• Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.

Project: Neura Belden BHVA (Belden HI vision Virtual Assistance)

• Translated AI agent concepts into 4 distinct user workflows (General, Redundancy, Network, VLAN agents).
• Designed 6 game-changing features with technical specifications: Predictive Issue Resolution, Cost Optimization Engine, Compliance Automation, Cross-Agent Orchestration, Knowledge Transfer Mode, Root Cause Analysis 2.0.
• Contributed reusable UX patterns and lightweight standards for experimentation-first AI environment: single-file HTML/CSS/JS prototypes, semantic accessibility, responsive grid layouts, and clear component structure enabling rapid future POC iterations.
• Translated early AI concepts into intuitive user workflows and interaction models for 4 specialized agents, focusing on clarity, trust-building, and progressive disclosure to surface complex AI capabilities.
• Documented design trade-offs in accessible language for non-designers.

• ▶ Built real-time voice assistant (STT → LLM → TTS pipeline) benchmarking and evaluating multiple STT providers including faster-whisper and Azure Speech. achieved sub-3s latency, Groq API (Llama 3) with multi-turn memory - directly handling edge cases in dictation, names and passcode recognition.

Enterprise AI content categorization and AI-powered web research.

• Built multi-LLM evaluation framework with annotated data
• Iterated LLM error rates based on annotated datasets
• Implemented AI-powered web research pipeline Stack: LLM, evals, OpenRouter, Python, Node.js, TypeScript, React

As the Solution Architect for Agentic AI in auto claims processing, I led global customer delivery implementations, encompassing solution design and detailing, multi-tenancy, process flows, integration with third-party solutions, and localization requirements.

• Architectural Analysis: Conducted in-depth analysis of business requirements, managing requirements and creating detailed specifications.
• Service Definition: Developed comprehensive technical definitions for services and integration contracts.
• AI Process Management: Automated AI process management, focusing on analysis, optimization, and continuous improvement.
• Requirements Gathering: Facilitated requirement-gathering sessions and analyzed business processes to identify optimization opportunities.
• Agile Collaboration: Employed agile methodologies, working closely with stakeholders to ensure alignment and responsiveness.
• Technical Support: Assisted senior management with technical analyses and deliverability assessments.

Conception, planning, and production deployment of two AI platforms for industrial research and engineering workflows, from use-case identification and requirements analysis through architecture decisions and build-vs-buy trade-offs to go-live.

• awRAG.io: Identification of the use case (fragmented knowledge base across distributed AI tools), definition of data requirements, architecture decision for a multi-tenant RAG-as-a-service platform with GDPR-compliant EU infrastructure and production-grade retrieval pipeline

• LAIERS.ai: Use-case definition (context loss in linear AI workflows), strategic product decisions on UX, cost structure, and multi-LLM orchestration, rollout of a spatial AI conversation platform with proprietary context management system LAICS

• LLMOps ownership: Quality assurance, pipeline optimization, security architecture (OAuth 2.0, SOC 2), and performance monitoring of both platforms in live production

• Core topics: LLM, RAG, vector databases, LLMOps, AI architecture strategy, cloud infrastructure, data sovereignty

• Co-founded an AI venture focused on building production-grade LLM applications and agentic systems
• Designed and implemented multi-agent AI workflows for financial and trading applications
• Developed LLM-powered copilot architectures for portfolio analysis, trade management, and personalized user coaching
• Built on-device and edge-deployed inference applications, optimizing models for low latency, privacy, and resource-constrained environments
• Led system architecture decisions across model selection, orchestration, state management, and deployment

• Conducting market analyses for GenAI solutions
• Developing the go-to-market strategy for an international GenAI/RAG solution
• Supporting the recruitment of offshore specialists in India
• Supporting the sales team

• Reshaped the company’s long-term strategic direction, crafting a purpose, vision and mission to create a purpose-driven performance culture
• Completed the next critical milestone for the NextGen aircraft development by heading the program management, fostering collaboration of a cross-functional team and validating requirements through a customer advisory board
• Led the M&A process for the divestment of a business unit, including due diligence, valuation, and negotiation with buyers
• Boosted revenue growth potential by refining the core business model and identifying aftersales as a key growth stream
• Led a high-performing team, mentoring five direct reports to deliver on strategic initiatives

• Ensuring the overall project goal with the Product Owner
• Onboarding a new service provider, including sourcing and contract negotiations with relevant stakeholders (e.g., procurement, legal)
• Implementing and executing the necessary governance processes
• Integrating new extraction software/GenAI tools to improve data extraction and processing within a document management workflow
• Ongoing agile management of deliverables, including building the project organization and controlling time, quality, and costs in the context of the overall project
• Sub-project organization, especially requirements management, structuring, agile planning, and prioritization
• Organizing and optimizing workstream communication for an end-to-end process
• Facilitating and leading meetings and workshops
• Change management consulting through targeted influence, coaching, and incentive systems

• Presentation introducing generic AI and large language models
• Explanation of legal frameworks (EU AI Act, US CLOUD Act, GDPR)
• Systematic review of AI tools along the SDLC and holistic systems
• Comparison of on-prem LLMs vs. cloud-based, as well as change management and works council
• Facilitated the discussion and derived next steps for introducing AI development tools