Senior AI / Machine Learning Engineer

We are looking for a Senior AI / Machine Learning Engineer with a strong software engineering and backend development background.

Full-time | Remote | Long-term engagment

Start: ASAP

Candidate location: EU

This is a hands-on engineering role combining Machine Learning, LLMs, backend development, and MLOps. We are looking for someone who can take an AI/ML use case, build the required solution in Python, integrate it with existing services and workflows, and make sure it can be run, tested, and maintained in a production environment.

The ideal candidate has strong experience with Python and backend development, a solid understanding of Machine Learning, and practical experience working with LLM-based solutions. Experience with AWS is required, while knowledge of Docker, Kubernetes, and MLOps is important for working effectively with the existing engineering and platform teams.

Our current focus is increasingly moving towards LLM-powered solutions. We are particularly interested in engineers who have practical experience integrating LLMs into real applications and business workflows. Experience with technologies such as OpenAI, AWS Bedrock, or Google Gemini is highly relevant, as well as experience with RAG, embeddings, model adaptation, or fine-tuning.

At the same time, this is not a research-focused or prompt-engineering role. We need someone with strong engineering skills who can build the backend and internal services around AI capabilities. Experience with APIs, databases, asynchronous processing, authentication/authorization, and writing maintainable production code is important.

You should also have a good understanding of MLOps and the ML lifecycle, including how models and AI services are developed, tested, deployed, monitored, and maintained. Hands-on experience with Docker and basic Kubernetes knowledge is expected.

Deep DevOps expertise is not required. Our Application Platform Team is responsible for most of the infrastructure and production deployment. However, you should be comfortable working with cloud-based environments, understand the principles behind MLOps, and be able to independently run and test your solution locally using the appropriate development and containerization tools.

Experience with AWS is essential. Experience with services such as SageMaker is useful, particularly for traditional ML use cases, although the main focus of the team is now shifting towards LLM-based applications.

Experience building AI agents is not a requirement. What matters most is practical experience using LLMs as part of existing applications, internal services, and business processes.

Key requirements

Strong Python and software engineering skills

Significant backend development experience

Experience building APIs and internal services

Solid understanding of Machine Learning and ML model lifecycle

Practical experience with LLMs / Generative AI

Experience integrating LLMs into applications and workflows

Strong AWS experience

Understanding of MLOps

Experience with Docker and basic Kubernetes

Experience working with databases

Understanding of asynchronous programming and concurrency

Experience with authentication and authorization

Ability to run, deploy, and test solutions locally

Experience with OpenAI, AWS Bedrock, or Google Gemini

RAG and vector search

Embeddings

LLM evaluation and optimization

Fine-tuning / adapting foundation models

LangChain or LangGraph

MLflow, Kubeflow, SageMaker

Kafka, Redis, or similar distributed systems technologies