Senior Machine Learning Engineer

You will design, deploy, and scale end-to-end machine learning systems and generative AI applications.

Responsibilities

• Engineer end-to-end ML pipelines covering data ingestion, feature engineering, training, and automated promotion using MLOps stacks.
• Convert research code into production-grade microservices using Docker and Kubernetes with REST, gRPC, or event-driven APIs.
• Build full-stack AI applications by integrating model services with UI components and workflow engines to ensure low-latency delivery.
• Optimize model performance and cost through quantization, pruning, and tuning GPU/CPU auto-scaling policies.
• Implement comprehensive observability including real-time metrics, distributed tracing, and drift/bias detection.
• Partner with data scientists to prototype algorithms and provide guidance on scalability and production-readiness.

Required Skills

• 8+ years of experience in machine learning and model development.
• Strong expertise in Machine Learning algorithms and Large Language Models (LLMs).
• Hands-on experience with Databricks or similar distributed data platforms.
• Proficiency with MLOps frameworks such as Kubeflow or SageMaker.
• Experience using OpenAI SDK and modern generative AI frameworks.
• Practical knowledge of containerization using Docker and orchestration with Kubernetes.
• Ability to build scalable APIs using REST or gRPC.
• Deep understanding of data engineering, feature engineering, and model evaluation.
• Experience implementing monitoring and model performance tracking mechanisms.

Education

• Any Graduate degree in Computer Science, Data Science, or a related field.