MLOps Engineer; ML, Speech, NLP & Multimodal Expertise

Overview

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.

• Design and maintain CI/CD pipelines for automated model training, testing, and deployment.
• Build container orchestration solutions (Docker, Kubernetes) for model serving at scale.
• Develop Infrastructure as Code (Terraform, Cloud Formation) 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.

• 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.

Programming & Software Engineering:

• Python (Expert Level):
  Advanced proficiency in scientific computing stack (Num Py, Pandas, Sci Py, Scikit-learn).
• Version Control
  :
  Git workflows, collaborative development, and code review processes.
• Software Engineering Practices
  :
  Testing frameworks, CI/CD pipelines, and production-quality code development.

Machine Learning and Language Model Expertise:

• Traditional Machine Learning and Deep Learning Knowledge
  :
  Proficiency in classical ML algorithms (Naive Bayes, SVM, Random Forest, etc.) and Deep Learning architectures.
• Understanding of Transformer Architecture
  :
  Attention mechanisms, positional encoding, and scaling laws.
• Training Pipeline Knowledge
  :
  Data preprocessing for large corpora, tokenization strategies, and distributed training concepts.
• Evaluation Frameworks
  :
  
  Experience with standard NLP benchmarks (GLUE, Super
  
  GLUE, etc.) and custom evaluation design.
• Fine-tun…