About The Role

The role focuses on building, scaling, and maintaining the core machine learning infrastructure and production pipelines that power real-time predictive services. The team bridges the gap between theoretical data science and robust, high-availability software engineering, ensuring models operate reliably at scale.

The engineer in this position will collaborate closely with data scientists to optimize training workflows, deploy models to production environments, and establish rigorous monitoring systems to track model drift and performance metrics.

Key Responsibilities

Deploy machine learning models into high-throughput production environments using containerized microservices and orchestration tools like Kubernetes
Design and optimize automated ML pipelines (CI/CD for ML) spanning data preparation, model training, evaluation, and deployment
Build and maintain feature stores and data pipelines using technologies such as Spark, SQL, and Python-based ETL frameworks
Implement real-time and batch model monitoring solutions to detect data drift, concept drift, and latency anomalies
Optimize model inference performance through quantization, pruning, and leveraging hardware acceleration (GPUs/TPUs)

What We Are Looking For

3-6 years of experience as a Machine Learning Engineer or Software Engineer working directly on ML infrastructure and deployment
Strong proficiency in Python and experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX
Hands-on experience with cloud infrastructure (AWS, GCP, or Azure) and cloud-native ML platforms like SageMaker, Vertex AI, or Kubeflow
Solid understanding of software engineering best practices, including version control, unit testing, containerization (Docker), and CI/CD pipelines
Bachelor's or Master's degree in Computer Science, Data Science, or a related quantitative field
Bonus: Experience with vector databases, LLM orchestration frameworks (LangChain, LlamaIndex), or Triton Inference Server

Machine Learning Engineer

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