AI Engineer (all levels)

Secure Systems Engineering GmbH; Berlin; Vor Ort; EUR 60.000 - 90.000; Vollzeit

Benefits dieser Stelle

Flexible hybrid working

Comfortable travel policy

Continuous training programs

Zusammenfassung

Secure Systems Engineering GmbH in Berlin seeks an AI/ML Engineer to integrate pre-trained models into applications and backend services. The role emphasizes design and implementation of APIs, optimizing inference performance, and maintaining end-to-end ML pipelines. Candidates should have strong programming skills in Python, experience with ML libraries, and knowledge of cloud environments like AWS and Azure. A degree in Computer Science or related fields is required. The position offers flexible hybrid working conditions and continuous professional development opportunities.

Qualifikationen

• Strong programming skills in Python.
• Experience with ML libraries: scikit-learn, pandas, NumPy, Hugging Face Transformers.
• Proficiency in containerization using Docker and Kubernetes.

Aufgaben

• Integrate pre-trained AI/LLM models into applications and backend services.
• Design and implement APIs and microservices.
• Optimize inference performance and improve model speed, latency, and cost efficiency.
• Conduct data preprocessing and feature engineering.
• Document architectural decisions and implementations.

Kenntnisse

Python programming

scikit-learn

pandas

NumPy

Hugging Face Transformers

AWS

Azure

GCP

Docker

Kubernetes

Ausbildung

Degree in Computer Science, Mathematics, Engineering, or related fields

Tools

TensorFlow

PyTorch

FastAPI

MLflow

Weights & Biases

Airflow

Jobbeschreibung

Your benefits at SSE

State-of-the-art IT equipment enabling flexible hybrid working worldwide – at the client’s site, in our modern office in Berlin or from home

Centrally located offices, including access to the unique ThinkTank Campus in Berlin-Wannsee

Comfortable travel policy, plus free snacks and beverages in our offices

Flat hierarchies and room for your own ideas

International team spirit with recognition of both collective and individual successes

Early responsibility and creative freedom from day one

Structured onboarding with a buddy and experienced mentor

Continuous training programs and regular development discussions

Your role

Integrate pre-trained AI/LLM models (OpenAI, Anthropic, Google, Hugging Face, etc.) into applications and backend services

Design and implement APIs, microservices, and scalable model-serving architectures

Optimize inference performance to improve speed, latency, and cost efficiency

Build and maintain end-to-end ML pipelines for data processing and model deployment

Implement observability tools (logging, monitoring, alerts) for AI systems in production

Train, fine-tune, and evaluate machine learning models for specific use cases

Build custom ML models using TensorFlow, PyTorch, scikit-learn or similar

Conduct data preprocessing, feature engineering, and dataset augmentation

Optimize models through hyperparameter tuning and architecture refinement

Apply MLOps best practices for model lifecycle management

Conduct experiments and report on performance metrics

Software Engineering

Write clean, maintainable, well-documented, and production-ready code

Develop robust data pipelines for training and inference

Build RESTful / FastAPI-based APIs for model interaction

Collaborate with backend, frontend, and product teams to integrate AI features

Implement resilience patterns (error handling, retries, fallbacks)

Ensure high code quality through testing, code reviews, and CI/CD workflows

Work closely with product and engineering teams to define AI requirements

Partner with data scientists to operationalize research models

Stay up to date with the latest AI/ML/LLM research, frameworks, and tools

Document architectural decisions, model design, and implementation details

Mentor junior engineers and guide best practices in ML engineering

Your profile

Technical Skills

Strong programming skills in Python (required)

Experience with ML libraries: scikit-learn, pandas, NumPy, Hugging Face Transformers

Experience with cloud environments (AWS, Azure, GCP)

Experience integrating AI APIs (OpenAI, Anthropic Claude, Google AI, AWS Bedrock, etc.)

Knowledge of deployment strategies (batch, streaming, real-time serving, edge)

Hands‑on experience with model-serving frameworks (TensorFlow Serving, TorchServe, ONNX, FastAPI)

Proficiency in containerization (Docker, Kubernetes)

MLOps & Infrastructure

Experience with experiment tracking tools (MLflow, Weights & Biases, Neptune)

Understanding of cloud platforms (AWS, GCP, Azure) and their ML services

Familiarity with orchestration tools (Airflow, Kubeflow, Prefect)

Experience implementing CI/CD for ML systems

Nice to Have

Experience with LLM fine-tuning, embeddings, and prompt engineering

Knowledge of vector databases (Pinecone, Weaviate, Qdrant)

Experience with distributed training (multi-GPU, multi-node)

Understanding of model optimization (quantization, pruning, distillation)

Experience with reinforcement learning or AutoML

Publications or contributions to open-source ML/AI projects

Degree in Computer Science, Mathematics, Engineering, or related fields

Soft Skills

Strong problem-solving and analytical mindset

Clear communication skills, including explaining technical concepts to non-technical stakeholders

Ability to work independently in a fast-paced environment

High attention to detail and commitment to code quality

Passion for AI, ML, LLMs, and emerging technologies

Collaborative mindset with interest in mentoring teammates

Sounds exciting? Then feel free to reach out directly!