Collaborate closely with ML Scientists to productize and scale ML models, from experimentation to robust production systems
Design, build, and own large-scale, distributed machine learning systems for training, deployment, inference, and monitoring
Lead design discussions and architecture reviews; drive high-impact engineering decisions for ML platforms and applications
Mentor and coach junior engineers and peers on best practices in ML engineering, system design, and code quality
Develop and maintain reusable components, libraries, and tools to accelerate ML development lifecycle
Proactively identify areas for improvement in model performance, pipeline efficiency, data quality, or platform capabilities
Ensure scalability, observability, and fault-tolerance across all components of the ML stack
Promote engineering excellence by advocating for best practices in testing, CI/CD, infrastructure-as-code, and monitoring
Partner with stakeholders across Data Engineering, Product, Marketing, and Platform teams to align solutions with business goals
Stay up to date on advancements in MLOps, ML frameworks, distributed systems, and apply learnings to improve systems and processes

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
8+ years of experience in software/ML engineering with a proven track record of delivering ML solutions at scale
Strong programming skills in modern languages such as Python, Scala, or Java
Deep experience in building and maintaining production-grade ML pipelines and infrastructure
Expertise in MLOps practices, including model lifecycle management, versioning, monitoring, and CI/CD for ML
Experience with big data ecosystems (e.g., Spark, Hive, Databricks, Delta Lake) and streaming technologies
Proficient in working with ML frameworks like TensorFlow, PyTorch, XGBoost, or similar
Experience working in cloud-based environments (AWS, GCP, or Azure) and with infrastructure-as-code tools
Hands-on experience with orchestration tools like Flyte, Airflow, Kubeflow, etc.
Proficient in containerization and orchestration technologies like Docker and Kubernetes

Senior Machine Learning Engineer

Key skills