Senior Staff / Principal ML Engineer - Building & Scaling Enterprise LLM Solutions | Remote

Company Description

Destinare Services is a global IT recruitment firm specializing in connecting top global tech companies with highly skilled IT professionals. We focus on the latest industry trends, remote-only job opportunities, and end-to-end talent solutions. Our mission is to deliver fast, reliable, and high-quality hiring support to help organizations scale efficiently while providing candidates access to premium global career opportunities. Visit www.destinareservices.com for more details.

Job Role

We are seeking a hands-on Senior Staff Machine Learning Engineer to lead cross-functional teams building and deploying large-scale LLM and ML systems. This role drives the full AI lifecycle—from research and model training to production deployment—while mentoring engineers and collaborating with research, product, and infrastructure leaders.

This position is ideal for technical leaders who remain deeply hands-on, enjoy building and optimizing large-scale training pipelines, and operate at the intersection of research, engineering, and product delivery.

Key Responsibilities

• Lead and mentor ML engineers, data scientists, and MLOps professionals

• Own end-to-end delivery of LLM and ML projects, from data to deployment

• Collaborate with Research, Product, and Infrastructure teams on goals and milestones

• Provide technical direction on distributed training, fine-tuning, and system design

• Implement best practices in MLOps, CI/CD, experiment tracking, and model governance

• Manage compute resources, budgets, and responsible AI standards

• Communicate progress, risks, and outcomes to stakeholders

Maintain

Required Skills & Qualifications

• Strong expertise in Machine Learning, NLP, and deep learning (Transformers, LLMs)

• Hands-on experience with PyTorch, TensorFlow, Hugging Face, or DeepSpeed

• Production deployment experience using Docker

• Proven leadership in delivering ML/LLM models to production

• Experience with distributed training, GPU/TPU optimization, and cloud platforms (AWS, GCP, Azure)

• Familiarity with MLOps tools such as MLflow, Kubeflow, or Vertex AI

• Excellent communication and cross-functional collaboration skills

• Bachelor’s or Master’s in Computer Science or related field (PhD preferred)

Other Details

• Fully remote role

• Contractor assignment (no medical or paid leave)

• Contract duration: 3-6 months (expected start: next week)

• Work commitment: 8 hours/day with 6-hour PST overlap (12:00 PM PST – 6:00 AM PST)

Interview Process

• Technical Interview (60 minutes)

• Take-home Assessment

• Final Interview (Technical + Cultural discussion)