ML/AI Engineer

Location: Helsinki, Finland

Department: Machine Learning and AI

Location Type: HYBRID

Employment Type: FULL_TIME

• Run discussions and workshops with clients to identify and evaluate AI/ML use cases with a clear business value lens.
• Assess feasibility, required data, technical constraints and risks.
• Propose end-to-end solution designs (e.g. LLM applications, predictive models, recommendation systems, optimisation setups).

• Implement models using Python and modern ML frameworks (e.g. PyTorch, TensorFlow, JAX or similar).
• Build robust training, evaluation and inference pipelines.
• Work with both classical ML and modern deep learning, depending on the problem.

• Design and implement solutions using LLMs.
• Build RAG-style systems with vector databases and orchestration frameworks.
• Evaluate LLM-based solutions rigorously instead of relying on hype.

• Deploy production-grade ML/AI systems on cloud platforms (AWS, Azure or GCP).
• Implement MLOps practices: experiment tracking, model registry, CI/CD for ML, monitoring and retraining.
• Use tools such as MLflow, Vertex AI, SageMaker, Azure ML, Docker and Kubernetes where relevant.

• Prepare and manage data used for models when needed (ETL/ELT, feature engineering, basic data pipelines).
• Collaborate closely with data engineers on data models and data platform choices, but remain able to do pragmatic data work yourself when required.

• Help them understand where AI adds real value, and where simpler solutions are better.
• Translate business requirements into technical delivery plans and explain technical trade-offs clearly.
• Advise clients on responsible AI, governance, model monitoring and reliability.

• Join early client conversations to shape AI initiatives and proposals.
• Help scope work, estimate effort and demonstrate solutions.
• Advocate for practical, outcome-focused AI adoption, not “AI for the sake of AI”.

• • Solid experience deploying ML/AI systems into production.
  • Deep skills in Python and modern ML frameworks (PyTorch, TensorFlow, JAX or similar).
  • Experience with end-to-end ML pipelines from data to inference.

• • Hands-on work with LLMs (OpenAI, Anthropic, Gemini, open-source models, etc.).
  • Experience with vector databases, RAG architectures and LLM application frameworks.
  • Understanding of LLM evaluation, prompting and basic LLMOps principles.

• • Strong programming habits: version control (Git), testing, code structure, reviews.
  • Experience with containerisation (Docker) and preferably some exposure to Kubernetes.
  • Comfortable using AI coding assistants (e.g. Copilot, Cursor, Claude Code, Gemini Code) in a deliberate way.

• • Experience with one or more cloud ML platforms: SageMaker, Vertex AI, Azure ML or similar.
  • Familiarity with MLflow, Kubeflow or other MLOps tooling is a plus.
  • Understanding of model monitoring, drift detection and lifecycle management.

• • Ability to work with typical data engineering tools and patterns (ETL/ELT, batch vs. real-time, dbt, Airflow or similar) at least at a practical level.
  • You do not need to be a pure data engineer, but you should understand how data platforms are built and operated.
  • Relevant cloud/ML certifications (e.g. AWS ML Specialty, GCP ML, Azure AI, Databricks ML) are beneficial but not required.

• Comfortable speaking with CxO-level and business stakeholders, not only technical teams.
• Experience leading or co-leading workshops, requirements discussions and solution scoping.
• Ability to translate ambiguous business problems into realistic ML/AI solutions and delivery plans.
• Willing to be hands-on in implementation; this is not a research-only or slide-only role.
• Calm and proactive in ambiguous consulting environments with shifting requirements.
• Collaborative, straightforward and able to work in a small, evolving company setting.

• You get to influence how AI is used in multiple organisations, not just one internal product.
• You own a large part of the lifecycle: from use case discovery and design to production deployment and operations.
• You work with the full scope of AI: LLMs, applied ML, optimisation and other methods – always tied to real business outcomes.
• You have strong influence over models, tools, MLOps stack and architectures, as long as they support the client’s goals.
• You help clients move away from hype towards practical, value-driven AI.

• Location: You must be based in Finland and have a valid work permit in Finland.
• Office presence: Ability to visit our Helsinki office roughly once a week (sometimes more depending on client and project needs).