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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and deploying end-to-end ML systems and production-grade LLM applications, with a strong focus on autonomous Agentic AI systems and cross-functional leadership. Proven ability to translate business objectives into technical roadmaps while maintaining high standards for code quality and system reliability.
Highest-signal resume keywords
End-To-End ML Systems DesignProduction-Grade LLM ApplicationsAgentic AI System ArchitectureCross-Functional Initiative LeadershipDeep-Dive Code/Architecture Reviews
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning EngineeringLLM IntegrationInfrastructure as CodeSystem DesignObservabilitySafety/GardrailsMulti-Agent CoordinationStateful Workflow OrchestrationComplex Action ExecutionModel Fine-Tuning
Soft Skills
CollaborationTechnical ExcellenceProblem SolvingLeadership
Tools & Technologies
Vector DatabasesDistributed ComputingGPU OptimizationAutomated Evaluation Frameworks
Industry Keywords
Exploratory ResearchProduction-Hardened ServicesAdvanced RAG SystemsAgent Memory ArchitecturesCombinatorial Optimization AlgorithmsQuantum Computing
About the role
Key responsibilities & impact- Collaborate closely with scientists, engineers, and customers
- Apply exploratory research and practical implementation
- Design production-grade LLM architectures
- Develop autonomous Agentic AI systems
- Solve complex real-world problems and drive customer value
- Lead cross-functional initiatives and translate business objectives into technical roadmaps
- Conduct deep-dive code and architecture reviews
- Help foster a culture of technical excellence
Requirements
What you’ll need- Proven track record of designing, deploying, and maintaining end-to-end ML systems and production-grade LLM applications at scale
- 5+ years of professional experience in ML/AI Engineering
- Experience transitioning prototypes into production-hardened services
- Robust system design, observability, safety/guardrails, and infrastructure as code
- Hands-on experience architecting Agentic AI systems, including multi-agent coordination, stateful workflow orchestration, autonomous decision loops, and complex action execution
- Strong background in LLM integration, including function calling/tool-use, structured output parsing, and advanced reasoning strategies
- Ability to lead cross-functional initiatives, translate business objectives into technical roadmaps, and uphold high standards for code quality and system reliability
- Experience leveling up high-performing engineering teams, conducting deep-dive code/architecture reviews, and fostering technical excellence
- Experience in LLM infrastructure, advanced RAG systems, model fine-tuning, agent memory architectures, and automated evaluation frameworks is a plus
- Proficiency with vector databases, distributed computing, and GPU optimization is a plus
- Familiarity with combinatorial optimization algorithms or the intersection of ML and Quantum Computing is a plus
Benefits
Comp & perks- Hybrid work arrangement in Helsinki or Stockholm
- Full-time employment