LLM Engineer Manager

This is a remote position.

We are seeking a hands-on Machine Learning Engineering Manager to lead cross-functional teams building and deploying cutting-edge LLM and ML this role youll drive the full lifecycle of AI development from research and large-scale model training to production deployment while mentoring top engineers and collaborating closely with research and infrastructure leaders.

Youll combine technical depth in deep learning and MLOps with leadership in execution and strategy ensuring that Turings AI initiatives deliver reliable high-performance systems that translate research breakthroughs into measurable business impact.

This position is ideal for leaders who are still comfortable coding optimizing large-scale training pipelines and navigating the intersection of research engineering and product delivery.

Roles & Responsibilities

• Lead and mentor a cross-functional team of ML engineers data scientists and MLOps professionals.
  
• Oversee the full lifecycle of LLM and ML projects from data collection to training evaluation and deployment.
  
• Collaborate with Research Product and Infrastructure teams to define goals milestones and success metrics.
  
• Provide technical direction on large-scale model training fine-tuning and distributed systems design.
  
• Implement best practices in MLOps model governance experiment tracking and CI/CD for ML.
  
• Manage compute resources budgets and ensure compliance with data security and responsible AI standards.
  
• Communicate progress risks and results to stakeholders and executives effectively.
  

Required Skills & Qualifications

• 9 yrs of strong background in Machine Learning NLP and modern deep learning architectures (Transformers LLMs).
  
• Hands-on experience with frameworks such as PyTorch TensorFlow Hugging Face or DeepSpeed
  
• 2 yrs of proven experience managing teams delivering ML/LLM models in production environments.
  
• Knowledge of distributed training GPU/TPU optimization and cloud platforms (AWS GCP Azure).
  
• Familiarity with MLOps tools like MLflow Kubeflow or Vertex AI for scalable ML pipelines.
  
• Excellent leadership communication and cross-functional collaboration skills.
  
• Bachelors or Masters in Computer Science Engineering or related field (PhD preferred).
  

Nice to Have

• Experience training or fine-tuning foundation models.
  
• Contributions to open-source ML or LLM frameworks.
  
• Understanding of Responsible AI bias mitigation and model interpretability.