Senior GenAI Engineer

Jobtailor; Deutschland; Remote; EUR 90.000 - 150.000; Vollzeit

Zusammenfassung

Jobtailor seeks an accomplished Architect to design and deliver end-to-end Generative AI and agentic‑AI systems. You will craft LLM-powered workflows, build intelligent agents with LangChain/LangGraph, and own full ML/GenAI pipelines from training to deployment.

You will collaborate with customers, product, and engineering to translate business needs into robust AI architecture, mentor engineers, and advance an enterprise AI platform strategy in a fast-paced startup context.

Qualifikationen

• 6+ years in traditional ML, incl. 2+ years with Generative AI.
• Strong experience with LLMs, prompt engineering, and agentic systems.
• Real-world LangChain/LangGraph experience or similar.
• Strong Python skills for API wrappers and integrations.
• Foundations in Transformers, CNNs, RNNs; hands-on TF, PyTorch, Scikit-learn.
• NLP, embeddings, vector databases, and OpenAI/Llama style models.
• Distributed cloud-native architectures (microservices, REST APIs).
• Proficiency with AWS/Azure/GCP and Docker/Kubernetes.
• MLOps/LLMOps covering training, deployment, monitoring, lifecycle.
• Excellent communication; capable of simplifying depth for stakeholders.

Aufgaben

• Architect and build scalable Generative AI applications end to end.
• Design LLM-powered workflows and multi-agent systems with reflexive behavior.
• Develop AI agents using LangChain/LangGraph for NL-to-SQL and RAG pipelines.
• Select, fine-tune, and optimize state-of-the-art LLMs.
• Own ML/GenAI pipelines: training, deployment, monitoring, lifecycle.
• Build APIs, microservices, and enterprise integration frameworks.
• Champion responsible AI: mitigate hallucinations, bias, reliability risks.
• Collaborate with customers, product, and engineering to translate needs.
• Mentor engineers and shape long-term AI platform strategy.
• Engage in customer-facing solution architecture and hands-on delivery.

Kenntnisse

Generative AI Development

LLM Prompt Engineering

LangChain/LangGraph Frameworks

Python Programming

MLOps Lifecycle Management

TensorFlow

PyTorch

NLP

APIs

Docker

Kubernetes

Vector Databases

OpenAI

Ausbildung

Bachelor's in CS

Master's in Data Science

Tools

AWS

Azure

GCP

Docker

Kubernetes

OpenAI

Llama

Vector Databases

APIs

Microservices

Jobbeschreibung

• Architect and build scalable Generative AI and agentic AI applications, end to end


• Design LLM-powered workflows and prompt strategies for reflexive, self-learning, multi-agent systems


• Build intelligent AI agents using LangChain and LangGraph for NL-to-SQL, autonomous task agents, and RAG pipelines


• Select, customize, fine-tune, and optimize state-of-the‑art LLMs


• Design and own full ML/GenAI pipelines, including training, deployment, monitoring, and lifecycle management


• Build APIs, microservices, and integration frameworks to bring AI into enterprise products


• Champion responsible AI practices by mitigating hallucinations, bias, and reliability risks


• Partner directly with customers, product, and engineering to turn business needs into robust AI architecture


• Mentor engineers and help shape the long‑term AI platform strategy


• Serve in a customer‑facing role spanning solution architecture, hands‑on engineering, and client conversations

Requirements

• 6+ years in traditional ML, including 2+ years hands‑on with Generative AI


• Strong experience with LLMs (GPT and similar), prompt engineering, and agentic systems


• Real‑world experience with LangChain/LangGraph or similar agentic frameworks


• Strong Python skills — API wrappers, third‑party integrations, internal tooling


• Solid foundation in Transformers, CNNs, RNNs — hands‑on with TensorFlow, PyTorch, Scikit‑learn


• Experience with NLP, embedding models, and vector databases


• Hands‑on work with OpenAI, Llama/Llama2, Azure OpenAI, and other open‑source models


• Experience designing distributed, cloud‑native architectures (microservices, REST APIs)


• Proficiency with AWS, Azure, or GCP, plus Docker/Kubernetes


• MLOps/LLMOps experience — training, deployment, monitoring, lifecycle management


• Excellent communication skills — you can translate technical depth for non‑technical stakeholders


• Bachelor's or Master's in CS, Data Science, Engineering, Math, Statistics, or related field


• Comfort with startup pace and strong ownership mentality


• Preferred: LLM fine‑tuning experience (LoRA, RLHF, PEFT)


• Preferred: Performance optimization (GPU/TPU acceleration, quantization, pruning, distillation)


• Preferred: AI observability/monitoring tool experience


• Preferred: Familiarity with AI governance and compliance (GDPR, SOC 2)


• Preferred: Prior consulting or solution‑architecture experience shipping enterprise AI products


• Preferred: Background in financial services, healthcare, or insurance

Core Competencies

Demonstrates expertise in architecting and building scalable Generative AI applications, with a strong focus on LLMs, prompt engineering, and ML pipeline management. Proficient in developing intelligent AI agents and integrating AI solutions into enterprise products while ensuring responsible AI practices.

Highest‑signal resume keywords

• Generative AI Development


• LLM Prompt Engineering


• LangChain/LangGraph Frameworks


• Python Programming


• MLOps Lifecycle Management

Hard Skills

• Generative AI


• LLMs


• Prompt Engineering


• LangChain


• LangGraph


• Python


• TensorFlow


• PyTorch


• NLP


• MLOps

Soft Skills

• Excellent Communication


• Mentoring

Certifications & Qualifications

• Bachelor's in CS


• Master's in Data Science

Industry Keywords

• AI Governance


• Compliance


• Financial Services


• Healthcare


• Insurance

Tools & Technologies

• AWS


• Azure


• GCP


• Docker


• Kubernetes


• OpenAI


• Llama


• Vector Databases


• APIs


• Microservices