Some AI challenges are about choosing the right model. Others require looking beyond it: at the systems, data, processes, and people involved. At Helmes, you’ll meet both – across many industries.
You’ll design production-grade AI solutions for enterprise clients and stay involved from discovery to production. You won’t own routine implementation or operations, but you’ll stay close enough to the technology to make sound decisions and help teams build solutions that work in practice.

Variety: 500+ clients across industries such as logistics and supply chain, telecom, manufacturing, finance, and the public sector, each with its own business needs and technical challenges.
Enterprise complexity, startup thinking: Work on large-scale projects in an environment that encourages curiosity, initiative, and fresh ideas.
Real ownership: Make architectural decisions and shape how AI solutions are designed and implemented.
Strong engineering environment: Collaborate with experienced architects, developers, analysts, and consultants, drawing on 35+ years of experience in complex software development.
Assess technical feasibility, data suitability, constraints and delivery risks, then translate the findings into a viable solution direction
Design end-to-end AI solutions across data, AI capabilities, applications, integrations and infrastructure
Define staged architectures for prototypes, MVPs and production systems
Make and document trade-offs across reliability, latency, cost, security and maintainability
Validate critical design choices through prototypes, reference implementations and deployment artefacts (e.g. repositories, datasets, deployment scripts)
Guide engineering teams during implementation and refine the architecture when evidence requires substantial design changes
Provide reusable documentation, code and technical guidance that let delivery teams proceed independently
Significant experience across software, AI delivery, including substantial responsibility for designing and evolving production AI solutions
Experience designing AI-enabled systems from uncertain requirements through production use
Strong architectural judgement across software, data, cloud and AI technologies
Ability to understand unfamiliar domains and map their processes, decisions, systems and data
Hands-on ability to analyze data, build working prototypes and deploy them into realistic environments
Experience evaluating AI system reliability and revising designs based on user feedback, operational evidence and trade-offs across latency and cost
Broad knowledge of AI capability groups (e.g. perception, classification, generation, recommendation, decision support, automation and agent-based workflows), with hands-on depth in at least one area
Experience with enterprise integrations (e.g. APIs, MCP integrations, identity services, business applications or data platforms) and at least one major cloud platform (AWS, Azure or GCP)
Experience working with structured and unstructured data (e.g. tabular data, documents, text, images or sensor data)
Ability to communicate architectural choices and produce artefacts that engineering teams can reuse
Nice to have
Experience in consulting, direct client-facing solution design or technical pre-sales
Industry-specific experience (e.g. supply chain, finance, public sector or manufacturing) or expertise in advanced AI use cases
Knowledge of AI compute and deployment constraints, including accelerator requirements, memory, throughput and hosting options
Experience creating reusable deployment artefacts, such as infrastructure as code, container definitions or automated deployment pipelines
Experience with sovereign, on-premises or hybrid deployments (e.g. regulated data environments, private cloud or restricted-connectivity settings)
Leadership or mentoring experience
Good to know
Location: Baltics, Finland or Poland (Tallinn preferred)
Onboarding: Your first months will include time with the team in Tallinn
If you want to design AI solutions and ensure they make an impact, let's chat!