Principal / Senior Lead Machine Learning Engineer (10+ Years Experience)

We are looking for a visionary Senior Machine Learning Engineer with over 10 years of professional software and machine learning experience. You will lead the design, architecture, and production deployment of scalable machine learning systems.In this role, you will bridge the gap between experimental research and robust production systems. You will mentor other engineers, drive technical direction, and build high-performance AI infrastructure that serves millions of users. Key Responsibilities: - Architect and Scale: Design, build, and deploy end-to-end machine learning pipelines and large-scale AI systems in production environments. - Productionize Models: Partner with data scientists to translate prototype code into high-performance, maintainable, and reliable services. - Establish MLOps: Mature our MLOps practices, including automated model deployment, monitoring, CI/CD, data drift detection, and governance. - Technical Leadership: Lead technical and architectural roadmaps, conduct design reviews, and define engineering best practices. - Mentor and Guide: Mentor senior and mid-level engineers, fostering a culture of technical excellence and continuous learning. What You Need to Have: - Experience: 10+ years of industry experience in software engineering, machine learning engineering, or AI systems architecture. - Programming Skills: Expert-level proficiency in Python and strong software engineering fundamentals (OOP, clean code, design patterns). - ML Frameworks: Deep hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face). - Cloud & MLOps: Proven experience with cloud platforms (AWS, GCP, or Azure) and production deployment tools (Docker, Kubernetes, MLflow, AWS SageMaker). - System Design: Strong foundation in distributed systems, data modeling, data pipelines (SQL, Spark, Kafka), and monitoring production failure modes. - Language: Speak English Fluently with native pronunciation. Nice-to-Have Skills: - Experience with large language models (LLMs), retrieval-augmented generation (RAG), or generative AI optimization. - Track record of publishing research or speaking at industry conferences. - Cloud certifications (AWS Solutions Architect, Machine Learning specialty, etc.).