--- title: 'ML/AI Engineer at Renessai' canonical: 'https://feeny.ai/job/ml-ai-engineer-renessai-helsinki-rct5s759sefa' type: 'job' last_seen: '2026-09-12' --- # ML/AI Engineer at Renessai - **Company:** Renessai - **Location:** Helsinki, Finland - **Employment:** full-time - **Work type:** hybrid - **Posted:** 2026-03-05 - **Last confirmed live:** 2026-09-12 - **Apply:** https://jobs.gem.com/renessai-com/am9icG9zdDrcVtJmi-dOh20suZ3qKBi- ## Job description ## 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. ## About Renessai ## Company Overview - **One-liner**: Renessai is a strategic AI consultancy that helps organizations navigate AI hype and build real business value by combining strategy, change leadership, and applied technical expertise. - **Entity Type**: Private (bootstrapped; no funding rounds disclosed) - **Headquarters**: Helsinki, Finland (also has offices in the United States and Berlin, Germany) - **Founded**: August 2024 - **Founders**: Mikko Koski, Niina Tuikka, Ossi Syd, Aapo Tanskanen, Eija-Leena Koponen, and Jussi Ritola ## Core Business - **Primary industry**: Business Consulting and Services (AI-focused) - **Target customers**: B2B; leadership teams and operating teams at Finnish and international organizations across media, engineering, transport, professional services, and more - **Mission**: Help companies “think beyond AI” by turning fuzzy AI ambition into concrete, measurable outcomes—combining technology, people, and processes to create lasting business value. ## Products & Services - **AI Strategy & Roadmap**: Prioritization of AI investments, capability audits, and actionable transformation plans. - **AI Capability Assessment**: Deep-dive evaluations across data, analytics, AI capabilities, and operating models. - **AI Use‑Case Discovery & Prioritization**: Identifying and ranking high‑impact AI opportunities. - **Proof‑of‑Concept Design & Delivery**: Rapid, iterative builds to validate AI value before scaling. - **Generative AI Development**: Custom agents, enterprise knowledge search, automation, multi‑agent workflows. - **Non‑Generative AI Evaluation & Setup**: Classical ML/LLM solution design and deployment. - **Interim AI & Digital Leadership**: Acting CAIO/CDO mandates to embed AI capability within client teams. - **Change Leadership & Organizational Design**: Helping clients adapt roles, processes, and culture for sustained AI adoption. All engagements are custom‑built; Renessai delivers over 50 projects per year. ## Market Standing - **Valuation / Market Cap**: Not publicly available - **Key Metric**: 17 employees (as of mid-2026), with +21.4% year‑over‑year headcount growth (+3 people) - **Notable Investors/Partners**: No external investors disclosed; the firm is co‑founded and self‑funded by senior practitioners. - **Growth Signals**: - Opened a second office in Berlin, Germany, plus a US location within two years of founding. - Active hiring (AI Software Engineer, ML/AI Engineer, Strategy & Transformation Consultant posted in 2026). - Client base of 30+ organizations across multiple industries. - High repeat project volume (50+ projects/year) indicating strong client demand. ## Competitive Advantages - **Deep, pre‑hype experience**: The team brings 96+ combined years of AI deployment experience, much of it predating the current generative AI wave. - **Founder‑led delivery**: The same senior consultants who win the work do the work—no handoffs to junior teams. - **Outcome‑focused, anti‑hype ethos**: Refuses to sell shelfware or buzzword strategies; engagements are tied to revenue, cost, and risk metrics. - **Full‑stack AI capability**: Covers strategy, technical build, organizational change, and interim leadership—rare for a boutique consultancy. ## Strategic Focus - **Current priorities**: Building agentic workflows (tool design, context engineering, memory, planning, orchestration, guardrails) and scaling production AI systems for clients; expanding the team with AI Software Engineers and ML/AI Engineers to deepen technical delivery. - **Growth direction**: Continue hiring in Helsinki and potentially other offices, maintain a low‑bureaucracy culture, and grow through repeat client work and referrals rather than external fundraising. ## Why Work Here - **Culture**: Described as a team of “Expeditionaries”—people who tackle unfamiliar problems, help each other, and own real work from day one. The firm is allergic to shelfware and buzzwords. - **Work model**: Hybrid (office in Helsinki at Bulevardi 6; Berlin and US offices also available). Openings list “Hybrid” in Helsinki. - **Engineering environment**: Engineers own real pieces of the stack, work directly with clients, and help define how AI‑native development works. Roles involve agent development, evaluation, cloud infrastructure, and cross‑functional collaboration. - **Perks**: Not detailed, but emphasis on ownership, direct client impact, and a flat structure where “people who win the work do the work.” - **Recent hiring signals**: Actively recruiting in 2026 for AI Software Engineer, ML/AI Engineer, and Strategy & Transformation Consultant—demonstrating growth and demand for impact‑driven talent. ## Sources 1. [renessai.com](https://www.renessai.com/careers) 2. [linkedin.com](https://linkedin.com/company/renessai) 3. [builtin.com](https://builtin.com/company/renessai) 4. [techbehemoths.com](https://techbehemoths.com/company/renessai) ## Other roles at Renessai - [AI Software Engineer](https://feeny.ai/job/ai-software-engineer-renessai-helsinki-jshqp6gcj42t) — Helsinki, Finland - [Open Application](https://feeny.ai/job/open-application-renessai-helsinki-0ap2byayfye1) — Helsinki, Finland - [Strategy & Transformation Consultant](https://feeny.ai/job/strategy-transformation-consultant-renessai-helsinki-1tq767jw4geq) — Helsinki, Finland - [ML/AI Engineer](https://feeny.ai/job/ml-ai-engineer-levio-toronto-j4880cn34s27) — Toronto, Canada