AI Architect
We’re looking for an AI Architect / Senior ML Engineer to design and deliver production-grade AI systems for enterprise clients.
This is a hands-on architecture and delivery role focused on building real-world AI solutions (not research). You’ll work across the full lifecycle of AI systems, from data pipelines and model development to deployment and monitoring in production environments.
You’ll combine classical machine learning, data engineering, and modern Generative AI (LLMs, RAG) with strong MLOps and cloud architecture practices. You’ll also work closely with consultants and clients to shape solutions early and ensure they are both technically sound and business-relevant.
What we offer
High-impact AI engineering role working across multiple industries
Close collaboration with consulting and engineering teams
Flexible, remote-friendly setup (Estonia, Latvia, Lithuania, Poland)
Opportunity to work with clients across Europe
Culture of autonomy, trust, and collaboration
Flat organization with direct access to decision-makers
Healthy work-life balance with flexible hybrid options
B2B contract setup
Regular team events and thoughtful company traditions
Helmes is rated 4.8/5 on Glassdoor, and 94% of employees would recommend us as a great place to work.
What you will do
Design and deliver end-to-end AI systems (data → models → deployment → monitoring)
Architect scalable AI solutions in AWS / GCP / Azure
Own MLOps, CI/CD pipelines, and production readiness of ML systems
Build reusable AI components, platforms, and accelerators
Integrate AI solutions into complex enterprise environments
Apply GenAI techniques (LLMs, RAG, fine-tuning) where relevant
Lead technical delivery from problem definition to production
Collaborate with consultants and clients on solution design
Guide engineering teams and promote AI-augmented development practices
What you will do
7–12+ years of experience, including 4–6+ years in ML/AI with production systems
Strong Python engineering skills (bonus: .NET / Java / C++)
Experience with PyTorch, SciKit-Learn, or TensorFlow
Strong background in data engineering and distributed systems (e.g., Spark, Kafka)
Hands-on experience with cloud platforms (AWS / GCP / Azure)
Solid MLOps experience (Docker, Kubernetes, CI/CD, MLflow or similar)
Experience with GenAI (LLMs, prompt engineering, RAG)
Strong system design and architecture skills
Ability to work with clients and take ownership of delivery
Fluent English
Nice to have:
Experience building AI platforms or internal developer tools
Consulting or client-facing delivery background
Multi-cloud experience
Experience with large-scale optimization or advanced AI use cases
Leadership or mentoring experience
Industry experience (finance, healthcare, logistics, etc.)