Machine Learning / MLOps

Job Summary

We are looking for a Machine Learning / MLOps with 5-10 years of experience to build and operate production-grade machine learning systems.

The role will focus on the complete ML lifecycle, from data preparation and model training to deployment, monitoring, optimization, and continuous retraining.

The ideal candidate will have strong software engineering fundamentals combined with hands-on experience in ML infrastructure, cloud platforms, model serving, automation, CI/CD, and scalable ML systems.

The role may support machine learning applications related to personalization, recommendations, search, content discovery, user engagement, and other intelligent product experiences.

Key Responsibilities

• Design and build scalable machine learning infrastructure and production ML systems.
• Own the deployment and operational lifecycle of machine learning models.
• Build automated pipelines for data processing, model training, validation, deployment, and retraining.
• Develop reliable ML workflows integrated with CI/CD processes.
• Partner with Data Scientists to take models from experimentation into production.
• Build scalable model-serving infrastructure for batch and real-time inference.
• Implement model monitoring, performance tracking, data-quality checks, and drift detection.
• Optimize ML systems for latency, scalability, reliability, and cost.
• Manage model versions, experiments, configurations, and deployment workflows.
• Develop APIs and microservices for ML inference.
• Troubleshoot production ML systems and perform root-cause analysis.
• Establish best practices for reproducibility, testing, observability, and model lifecycle management.
• Work with cloud infrastructure, containers, and distributed systems.
• Participate in architecture and system-design discussions.
• Mentor engineers and contribute to the development of ML engineering standards.

Required Qualifications & Skills

• 5-10 years of experience in Machine Learning , MLOps, ML Infrastructure, or Software Engineering with significant ML experience.
• Strong Python programming skills.
• Strong understanding of machine learning algorithms and the ML lifecycle.
• Hands-on experience deploying and operating ML models in production.
• Strong experience with AWS, GCP, or Azure.
• Experience with Docker and Kubernetes.
• Experience building ML pipelines and CI/CD workflows.
• Experience with one or more tools/frameworks such as: MLflow, Kubeflow, Airflow,SageMaker,Vertex AI
• Experience with REST APIs and microservices.
• Strong understanding of data pipelines and distributed systems.
• Experience with model monitoring and production observability.

Preferred Qualifications

• Experience with recommendation systems, personalization, ranking, search, or content discovery.
• Experience working with high-volume consumer-facing products.
• Experience with Spark/PySpark, Kafka, Databricks, or similar technologies.
• Experience with feature stores and real-time ML pipelines.
• Experience with real-time model inference and low-latency systems.
• Experience with LLM/GenAI model serving or ML infrastructure.
• Strong system-design and technical leadership experience.