AI Engineer (m/f/n)

InPost Group is an innovative European out of home deliveries company operating across several countries with a network of intelligent lockers providing fast, convenient, and secure parcel delivery. It is a publicly traded company with a market capitalization of about $5 billion as of March 2023 and employs over 10,000 people worldwide, focusing on sustainable and efficient delivery solutions.

• Minimum Bachelor's degree in Computer Science, Engineering, or related field.
• 7+ years of experience as a full-stack developer with focus on AI model integration.
• Proficiency in Python.
• Strong knowledge of GenAI models and LLMs including model selection, tuning, and embedding strategies.
• Experience with API development and integration.
• Working knowledge of Docker and Kubernetes.
• Familiarity with cloud platforms (AWS, GCP, or Azure) for AI model deployment.
• Proficient in vector databases and their integration with LLM models.
• Familiarity with database management (SQL, NoSQL) and caching solutions (e.g., Redis).
• Experience in CI/CD pipelines, code versioning (Git), and DevOps practices.
• Excellent knowledge of English and Polish.

Preferred Qualifications:

• Knowledge of streaming architectures (e.g., Apache Kafka).
• Familiarity with serverless architectures (e.g., AWS Lambda).
• Experience with ML frameworks like TensorFlow, PyTorch, or ONNX.
• Understanding of data privacy and security best practices in AI applications.

Soft Skills:

• Strong problem-solving skills.
• Ability to work independently and in a team.
• Excellent communication skills.
• Proactive approach to emerging AI technologies and frameworks.

Model Integration & API Development:

• Integrate LLMs and other GenAI models into web applications through efficient API design and implementation.
• Build and optimize API endpoints for real-time interaction between front-end applications and back-end AI models.
• Design and develop secure, scalable, and high-performing microservices for AI model deployment.

Back-End Development & AI Pipelines:

• Develop robust back-end systems in Python to support deployment, scalability, and maintenance of GenAI models.
• Build and maintain data pipelines including preprocessing data and post-processing AI model outputs.
• Implement best practices for handling sensitive data and maintaining model performance.

Infrastructure & Deployment:

• Use Kubernetes and Docker for containerization and orchestration of AI applications.
• Implement CI/CD pipelines for automated testing and deployment.
• Maintain scalable and secure cloud infrastructure using platforms such as Google Cloud Platform or Azure.

LLM and GenAI Ecosystem Expertise:

• Utilize vector databases (e.g., Pinecone, Weaviate, Faiss) for managing and retrieving embeddings.
• Work with frameworks including Hugging Face Transformers, LangChain, and OpenAI.
• Optimize and fine-tune LLMs to improve performance based on application needs.