The Role
Lead AI/ML discovery with clients, design end-to-end LLM and ML solutions, implement models and pipelines in Python, deploy and operate production ML in cloud using MLOps tools, collaborate with data engineers, guide responsible AI and support sales and workshops.
Summary Generated by Built In
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.
Skills Required
- 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 experience with LLMs (OpenAI, Anthropic, Gemini, open-source models)
- Experience with vector databases and RAG architectures
- Understanding of LLM evaluation, prompting and basic LLMOps principles
- Strong software engineering practices: version control (Git), testing, code reviews
- Experience with containerisation (Docker); exposure to Kubernetes is preferred
- Experience with cloud ML platforms (SageMaker, Vertex AI, Azure ML or similar)
- Familiarity with MLOps tooling (MLflow, Kubeflow or similar)
- Practical ability to work with data engineering patterns and tools (ETL/ELT, dbt, Airflow or similar)
- Understanding of model monitoring, drift detection and lifecycle management
- Comfortable engaging with CxO-level stakeholders and leading workshops and scoping
- Based in Finland with a valid work permit and able to visit Helsinki office roughly once a week
- Relevant cloud/ML certifications (AWS ML Specialty, GCP ML, Azure AI, Databricks ML) are beneficial
- Comfort using AI coding assistants (Copilot, Cursor, Claude Code, Gemini Code) deliberately
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The Company
What We Do
Renessai is a strategic AI consultancy based in Helsinki, Finland. Founded in 2024, the company helps organizations navigate AI hype by combining strategy, change leadership, and technical expertise to create measurable business value. They work with leadership teams to audit AI capabilities, prioritize use cases, and ensure concrete outcomes, rather than selling software or technology products.
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