Principal Machine Learning Engineer

• Design and deploy scalable LLM-powered systems and reusable backend APIs
• Bridge cutting-edge AI research with production-grade engineering
• Build agent-based systems, RAG pipelines, and workflow automation tools
• Develop capabilities across chatbots, text-to-code, and data interaction layers
• Partner with cloud and SRE teams to deliver robust, scalable architectures
• Implement distributed systems for training and inference (e.g., Ray, DeepSpeed)
• Drive best practices for ML systems, deployment, and performance optimization

• PhD in Computer Science, Mathematics, Statistics, or related field
• Strong experience as an IC in Machine Learning Engineering
• Proven track record in building and scaling ML/AI systems in production
• Deep understanding of ML fundamentals (NLP and/or Computer Vision)
• Experience with distributed systems (Ray, Horovod, DeepSpeed, etc.)
• Strong software engineering and system design fundamentals
• Experience owning ML services end-to-end in enterprise environments
• Ability to align technical work with business outcomes and communicate with stakeholders, * Experience with ML pipelines (Kubeflow, DVC, Ray)
• Familiarity with microservices (gRPC, GraphQL)
• Experience with LLM optimization (fine-tuning, quantization, PEFT)
• Knowledge of advanced prompting strategies (Chain-of-Thought, etc.)

If you’re interested in working on AI systems at a massive scale, with real adoption and immediate impact, this is a rare opportunity to do so within a highly respected, well-resourced environment.

We’re partnering with a globally recognized enterprise investing heavily in AI, building one of the most widely adopted internal LLM platforms at scale. The platform is used by a large global workforce and supports use cases such as chatbots, agent workflows, code generation, and data interaction, helping teams move from idea to production significantly faster.

This is a high-impact, highly visible role at the intersection of research and production, where you will help design and scale LLM-powered systems and reusable APIs used across the organization. The team operates in a fast-paced, execution-focused environment, taking problems from concept to production quickly with real adoption at scale.