ML/AI Engineer

_About the role_

We are looking for a Senior ML/AI Engineer to design, build and productionise AI systems that solve concrete business problems for our clients.

You will lead discovery for AI use cases, assess feasibility, and design end-to-end ML/AI solutions. You will then implement and deploy these solutions in production, working closely with data engineers, developers and business stakeholders.

This is a consulting role: you will both advise on the approach and work on the hands-on engineering yourself.

_What you will do_

Lead AI/ML discovery and solution design

• 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).

Design and build ML/AI systems

• 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.

Work with GenAI and LLM-based solutions

• 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.

Productionise and operate ML/AI in the cloud

• 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.

Handle data for ML (together with data engineers)

• 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.

Guide clients on responsible, value-driven AI

• 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.

Support sales and act as an AI advocate

• 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”.

_What you bring_

Technical skills

You do not need every item below, but you should be confident in several and willing to grow into the rest.

Strong ML/AI engineering background

• * 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.

GenAI / LLM experience

• * 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.

Software engineering practices

• * 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.

MLOps and cloud

• * 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.

Data-related skills

• * 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.

Consulting skills and mindset

• 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.

_Why this role might be interesting for you_

• 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.

_Practicalities_

• 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).

_How to apply_

If this sounds like you, send us your CV and we’re happy to tell you more! We review applications continuously.