AI Engineer – GenAI Platform, Mid-Level

Jobtailor; Deutschland; Remote; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

Jobtailor is seeking a Senior AI engineer to design and develop Generative AI services and multi-agent systems across global teams.

You will implement RAG, tool calling, LLM gateways, and optimize latency, cost, and observability while driving MLOps, CI/CD, and governance across data pipelines and deployments.

Strong engineering fundamentals and experience delivering GenAI projects in production are essential.

Qualifikationen

• Experience in Software Engineering and Applied AI.

Aufgaben

• Design and develop Generative AI services and multi-agent systems.
• Implement RAG, tool calling, and agent orchestration solutions.
• Integrate and operate models through LLM gateways.
• Develop LLMOps practices for monitoring, observability, and governance.
• Implement consumption measurement, chargeback, and inference cost optimization mechanisms.
• Build and maintain data pipelines for billing and metering.
• Apply MLOps, CI/CD, automated testing, and model lifecycle management practices.
• Respond to incidents and support monitoring, reliability, and continuous improvement of services.
• Collaborate with global Product, Engineering, Platform, Security, and Data teams.

Kenntnisse

GenAI Development

Multi-Agent Orchestration

Advanced Python Skills

CI/CD & Automated Testing

MLOps Practices

API Integration

Observability & Performance

LLM Prompting

Tools

LangChain

LangGraph

LiteLLM

AWS

Kafka

Terraform

Databricks

Delta Lake

DLT

Internal Developer Platforms

Jobbeschreibung

• Design and develop Generative AI services and multi-agent systems
• Implement RAG, tool calling, and agent orchestration solutions
• Integrate and operate models through LLM gateways
• Develop LLMOps practices for monitoring, observability, and governance
• Implement consumption measurement, chargeback, and inference cost optimization mechanisms
• Build and maintain data pipelines for billing and metering
• Apply MLOps, CI/CD, automated testing, and model lifecycle management practices
• Respond to incidents and support monitoring, reliability, and continuous improvement of services
• Collaborate with global Product, Engineering, Platform, Security, and Data teams

Requirements

• Experience in Software Engineering and Applied AI
• Hands-on experience delivering GenAI projects in production environments
• Advanced English proficiency
• Knowledge of GenAI and Agentic AI
• Knowledge of multi-agent orchestration, tool calling, and multi-step reasoning
• Knowledge of RAG and prompt engineering
• Experience with LangChain, LangGraph, or equivalent frameworks
• Knowledge of LLMOps, LLM platforms, and LLM gateways such as LiteLLM or similar tools
• Experience integrating models through APIs
• Knowledge of observability and evaluation for LLM-based applications
• Experience optimizing latency and performance
• Knowledge of vector databases and retrieval mechanisms
• Advanced Python skills
• AWS experience preferred
• Understanding of best practices in architecture, observability, and reproducibility
• Knowledge of MLOps, version control, and experiment tracking
• Experience with CI/CD, automated testing, and deployment and rollback of AI services
• Knowledge of batch and streaming data pipelines
• Experience with Spark and Lakehouse architectures
• Knowledge of data orchestration
• Nice to have: Kafka and event streaming; Terraform and Infrastructure as Code; Databricks, including Delta Lake and DLT; internal developer platforms; experience working in global and distributed environments

Demonstrates expertise in Generative AI services, multi-agent systems, and LLMOps practices, with a strong focus on optimizing performance and implementing robust monitoring and governance mechanisms. Proficient in software engineering principles and experienced in delivering AI projects in production environments.

Highest-signal resume keywords

• Generative AI Services Development
• LLMOps Practices
• Multi-Agent Orchestration
• Advanced Python Skills
• CI/CD and Automated Testing

ATS Optimization Keywords

Hard Skills

• Software Engineering
• Applied AI
• GenAI Project Delivery
• RAG and Prompt Engineering
• API Integration
• Latency and Performance Optimization
• MLOps
• Data Pipeline Development
• Spark and Lakehouse Architectures
• Vector Databases

Soft Skills

• Collaboration
• Incident Response
• Continuous Improvement

Industry Keywords

• Observability
• Governance
• Chargeback
• Inference Cost Optimization
• Data Orchestration
• Batch and Streaming Data Pipelines
• Experiment Tracking
• Version Control

Tools & Technologies

• LangChain
• LangGraph
• LiteLLM
• AWS
• Kafka
• Terraform
• Databricks
• Delta Lake
• DLT
• Internal Developer Platforms