AI Development Pipeline Engineer

About Monterro Monterro is a Stockholm-based private equity firm specializing exclusively in Nordic B2B software companies. We manage EUR 1.1 bn across four funds and are building a dedicated AI team to accelerate AI adoption and value creation across our 23+ portfolio companies. Website:  Role Overview We’re looking for an AI Development Pipeline Engineer to help our portfolio companies move rapidly from AI proof-of-concepts to reliable, maintainable production systems. You will act as a hands-on mentor and strategic advisor, guiding engineering teams and CTOs in modern LLM-centric development practices, evaluation, deployment, and monitoring. Engagements are short and focused (days rather than weeks). Your goal is to leave each portfolio company with a working, self-owned pipeline and a clear blueprint for future iterations.  Key Responsibilities Pipeline Design & Implementation  Design end-to-end workflows covering data acquisition, model training/fine-tuning (e.g., LoRA/PEFT), evaluation, deployment, and monitoring.  Select and integrate appropriate tooling (MLflow, SageMaker, Vertex AI, Kubeflow, Metaflow, Airflow, DVC, etc.) based on each company’s tech stack.  Package reference architectures and infrastructure-as-code templates to accelerate adoption.  Evaluation & Monitoring  Establish automated evaluation gates focusing on accuracy, relevance, latency, and cost.  Implement performance and drift monitoring dashboards with alerting and rollback strategies.  Data Strategy & Governance  Advise on dataset curation, versioning, feedback loops, and synthetic data generation where appropriate.  Ensure compliance with GDPR and license requirements for open-source models and datasets.  Deployment & Scaling  Deploy models as containerised microservices or batch jobs on cloud or on-prem infrastructure.  Optimise inference cost and latency through batching, quantisation, or model selection.  Enablement & Advisory  Run workshops and pair-programming sessions to upskill client engineers.  Provide architectural reviews, cost modelling, and build-vs-buy recommendations to CTOs.  Document best practices and hand over ownership cleanly at project end.  Continuous Tool Evaluation  Keep abreast of emerging LLM platforms, vector databases, and orchestration frameworks.  Share concise evaluations so portfolio companies can make informed choices quickly.