Senior Artificial Intelligence Engineer

IPI Technolab; Deutschland; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

IPI Technolab is seeking an experienced AI/ML Engineer to design, build, and deploy scalable AI solutions for real-world business needs. You will productionize Generative AI apps using LLMs, RAG, embeddings, and tool calling, and architect robust data pipelines and APIs.

The role requires strong Python, PyTorch, and cloud experience, plus hands-on with Docker, Kubernetes, and MLOps. Teaming with data engineers, software engineers, and product managers is essential.

Qualifikationen

• Bachelor's or Master's in CS/AI/Data Science or related field.
• 5+ years in software/ML/AI with production AI systems.
• Strong Python and software fundamentals.
• Hands-on with Generative AI, LLMs, RAG, embeddings, vector databases, and prompt engineering.
• Strong knowledge of PyTorch, Hugging Face, Transformers, and ML frameworks.
• Experience with LLM fine-tuning, PEFT/LoRA, inference optimization, evaluation and serving.
• Knowledge of ML, NLP, statistics, and model evaluation.
• Experience building and deploying production APIs and distributed services.
• Proficiency in Docker, Kubernetes, CI/CD, MLOps, and cloud infrastructure.
• Experience with AWS, Azure, or GCP; databases including PostgreSQL/Redis/NoSQL/vector DBs.
• Understanding of data pipelines, ETL/ELT, distributed systems, scalable data processing.
• Strong software architecture, system design, testing, security, and performance.

Aufgaben

• Design, develop, and deploy scalable AI/ML solutions for real-world business problems.
• Build and productionize Generative AI apps using LLMs, RAG, AI agents, embeddings, and tools.
• Develop and fine-tune models with PyTorch, TensorFlow, and Hugging Face.
• Design robust AI architectures for data ingestion, inference, orchestration, evaluation, and monitoring.
• Build high-performance AI/ML APIs and services with Python and FastAPI.
• Develop and optimize RAG pipelines: document processing, chunking, embedding, retrieval, reranking, and generation.
• Implement evaluation, observability, guardrails, and model quality monitoring.
• Deploy AI workloads on AWS, Azure, or GCP; apply MLOps and DevOps practices.
• Use Docker, Kubernetes, CI/CD, model versioning, experiment tracking, and automated deployments.
• Optimize AI systems for latency, scalability, reliability, GPU use, and costs.
• Collaborate with data/software engineers, product managers, and stakeholders to translate requirements.
• Conduct technical research and evaluate new AI models, frameworks, and architectures.
• Establish engineering best practices for testing, security, data privacy, and documentation.
• Mentor junior engineers and contribute to architecture decisions.
• Take ownership of AI projects from architecture to production.

Kenntnisse

Python

PyTorch

Hugging Face

Transformers

FastAPI

Docker

Kubernetes

CI/CD

MLOps

LLMs

RAG

Embeddings

Vector databases

Prompt engineering

Cloud platforms

Ausbildung

Bachelor's or Master's degree in Computer Science / AI / Data Science

Tools

Python

PyTorch

Hugging Face

Transformers

FastAPI

Docker

Kubernetes

CI/CD

AWS

Azure

GCP

PostgreSQL

Redis

NoSQL databases

Vector databases

Embeddings

Jobbeschreibung

Role & responsibilities:

• Design, develop, and deploy scalable AI/ML solutions for real-world business problems.
• Build and productionize Generative AI applications using LLMs, RAG, AI agents, embeddings, vector databases, and tool/function calling.
• Develop and fine-tune machine learning and deep learning models using frameworks such as PyTorch, TensorFlow, and Hugging Face.
• Design robust AI architectures covering data ingestion, model inference, orchestration, evaluation, monitoring, and continuous improvement.
• Build high-performance AI/ML APIs and services using Python and frameworks such as FastAPI.
• Develop and optimize RAG pipelines, including document processing, chunking, embedding, retrieval, reranking, and response generation.
• Implement LLM evaluation, observability, guardrails, hallucination mitigation, and model-quality monitoring.
• Deploy and operate AI workloads on cloud platforms such as AWS, Azure, or GCP.
• Apply MLOps and DevOps practices, including Docker, Kubernetes, CI/CD, model versioning, experiment tracking, and automated deployments.
• Optimize AI systems for latency, scalability, reliability, GPU utilization, and infrastructure cost.
• Work with data engineers, software engineers, product managers, and business stakeholders to translate requirements into production AI solutions.
• Conduct technical research and evaluate emerging AI models, frameworks, tools, and architectures.
• Establish engineering best practices around testing, security, data privacy, documentation, and code quality.
• Mentor junior and mid-level engineers and contribute to technical architecture and engineering decisions.
• Take ownership of AI projects from architecture and proof-of-concept through productio

Preferred candidate profile:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Engineering, or a related field.
• 510 years of professional software engineering, machine learning, or AI experience, with significant experience building production-grade AI systems.
• Strong proficiency in Python and solid software engineering fundamentals.
• Hands-on experience with Generative AI, LLMs, RAG, AI agents, embeddings, vector databases, and prompt engineering.
• Strong practical knowledge of PyTorch, Hugging Face, Transformers, and modern AI/ML frameworks.
• Experience with LLM fine-tuning, PEFT/LoRA, inference optimization, model evaluation, and model serving is highly desirable.
• Strong understanding of machine learning, deep learning, NLP, statistics, and model evaluation.
• Experience building and deploying production APIs and distributed services.
• Strong knowledge of Docker, Kubernetes, CI/CD, MLOps, and cloud infrastructure.
• Professional experience with at least one major cloud platform: AWS, Azure, or GCP.
• Experience with databases such as PostgreSQL, Redis, NoSQL databases, and vector databases.
• Understanding of data pipelines, ETL/ELT, distributed systems, and scalable data processing.
• Strong knowledge of software architecture, system design, testing, security, and performance optimization.