We’re looking for a Senior Machine Learning Engineer to bridge the gap between data science and production engineering.
You won't just be training models in a sandbox. You’ll be architecting scalable pipelines, optimizing inference for cost and latency, and ensuring reliability at scale, If you care about uptime, system design, and making sure your models actually deliver measurable value to the end-user, this is your kind of challenge.
What You’ll Do:
✅ Architect, train, and deploy production-grade ML systems from data ingestion to API serving
✅ Build and maintain robust ML pipelines for training, validation, and continuous serving
✅ Optimize models for performance, cost, and scalability (quantization, batching, caching, vector search)
✅ Implement comprehensive monitoring for data drift, model decay, and system health
✅ Mentor mid-level and junior engineers through code reviews, system design sessions, and pairing
✅ Establish best practices for MLOps, versioning, and reproducibility across the team
Who You Are:
✅ 5+ years in machine learning engineering or applied AI, with a proven track record of shipping to production
✅ Strong in Python, PyTorch/TensorFlow, and modern ML frameworks
✅ Deep experience with model deployment (FastAPI, Docker, Kubernetes, cloud endpoints)
✅ Proficient with MLOps tools (MLflow, Kubeflow, SageMaker, or similar)
✅ Strong software engineering fundamentals (testing, CI/CD, distributed system design)
✅ Fluent in English — written and spoken
✅ Pragmatic, systems-oriented, and passionate about building AI that works in the wild
