AI Engineer

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 Think Tank 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

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 Tensor Flow, 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

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

Strong programming skills in Python (required)

Experience with ML libraries: scikit-learn, pandas, Num Py, 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 (Tensor Flow Serving, Torch Serve, ONNX, FastAPI)

Proficiency in containerization (Docker, Kubernetes)

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

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

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!