MLOps Engineer (ML, Speech, NLP & Multimodal Expertise) | Uppsala or Stockholm

We are looking to hire a MLOps Engineer with strong expertise in machine learning, speech and language processing, and multimodal systems. This role is essential to driving our product roadmap forward, particularly in deploying, testing, evaluating and monitoring our core machine learning systems and developing next-generation speech technologies.

The ideal candidate will be capable of working independently while effectively collaborating with cross-functional teams. In addition to deep technical knowledge, we are looking for someone who is curious, experimental, and communicative.

Key Responsibilities

Essential

• Design and maintain CI/CD pipelines for automated model training, testing, and deployment.

• Build container orchestration solutions (Docker, Kubernetes) for model serving at scale.

• Implement deployment strategies (blue-green, canary, A/B testing) for safe model rollouts.

• Develop Infrastructure as Code (Terraform, CloudFormation) for reproducible ML environments.

• Optimize model serving infrastructure for latency, throughput, and cost efficiency.

• Manage model versioning, registry, and artifact storage systems.

• Build real-time monitoring dashboards for model performance, latency, and resource utilization.

• Implement automated alerting systems for model degradation and anomaly detection.

• Design feature drift detection and data quality monitoring for production traffic.

• Track business metrics and ROI analysis for model deployments.

• Build specialized inference pipelines for speech-to-text and text-to-speech models.

• Optimize speech model performance for real-time and batch processing scenarios.

• Design evaluation frameworks specific to speech quality metrics (WER, latency, naturalness).

• Handle multi-modal data pipelines combining audio, text, and metadata.

• Create feedback loops to capture user interactions and model effectiveness.

• Create automated retraining pipelines based on performance degradation signals.

• Develop business metrics and ROI analysis for model deployments.

• Implement experiment tracking systems (MLflow, Weights & Biases) for reproducibility.

• Design hyperparameter optimization frameworks for efficient model tuning.

• Conduct statistical analysis of training dynamics and convergence patterns.

• Create automated model selection pipelines based on multiple evaluation criteria.

• Develop cost-benefit analyses for different training configurations and architectures.

Additional Responsibilities

• Implement automated evaluation pipelines that scale across multiple models and benchmarks.

• Design comprehensive test suites with statistical significance testing for model comparisons.

• Develop fairness metrics and bias detection systems for speech models across demographics.

• Perform statistical analysis of training datasets to identify quality issues and coverage gaps.

• Create interactive dashboards and visualization tools for model performance analysis.

• Build A/B testing frameworks for comparing model versions in production.

• Build and maintain ETL pipelines using SQL, Azure, GCP, and AWS technologies.

• Design data ingestion systems for massive-scale speech and text corpora.

• Implement data validation frameworks and automated quality checks.

• Create sampling strategies for balanced and representative training datasets.

• Develop data preprocessing and cleaning pipelines for audio and text.