Senior Machine Learning Engineer

Description

The role

We are looking for a hands-on senior ML engineer to lead the development and operation of production-grade AI systems across LLMs, OCR, and voice. This role combines deep technical ownership with leadership of a high-performing team of junior engineers, as well as direct engagement with stakeholders to shape AI solutions.

Responsibilities

• Lead and mentor a team of highly talented junior ML engineers through:
  Code reviews, design reviews, and technical direction
  Enforcement of strong software engineering and ML best practices
• Design, deploy, and operate scalable AI systems with a focus on reliability and performance
• Lead production deployment of LLMs and multimodal systems (RAG, OCR, voice)
• Own model performance end-to-end, combining evaluation, observability, and hardware optimization:
  Build evaluation pipelines (benchmarks, regression testing, LLM-as-judge)
  Implement deep observability (tracing, latency, error tracking)
  Optimize GPU utilization (multi-GPU serving, batching, quantization, memory tuning)
  Continuously improve throughput, latency, and cost efficiency
• Architect and manage GPU infrastructure:
  Model serving, load balancing, and scaling strategies
  Hardware-aware deployment and performance tuning
• Build and maintain robust MLOps pipelines:
  Model/version management, CI/CD, automated testing, and rollback strategies
  Monitoring and feedback loops for continuous improvement
• Engage directly with clients and stakeholders to:
  Gather and clarify business requirements
  Translate non-technical needs into well-defined technical problems
  Communicate solutions, trade-offs, and progress through clear documentation, reports, and proposals
• Contribute hands-on to system design, implementation, debugging, and production incident resolution

Requirements

• Proven experience deploying LLMs in production
• Strong experience with GPU-based inference and optimization
• Experience with MLOps and production ML systems
• Experience with OCR/document AI and/or voice systems (STT/TTS)
• Experience with Docker and Kubernetes
• Strong understanding of modern AI architectures (RAG, vector DBs, agent workflows)
• Experience mentoring or leading engineers
• Strong communication skills with the ability to bridge business and technical domains

Nice to have

• Experience with open-weight models (Qwen, Llama, DeepSeek, Gemma)
• Experience with on-prem / sovereign AI deployments