Lead Assistant Manager

We are looking for a motivated ML Engineer / MLOps Engineer with strong foundational experience in building and supporting machine learning systems in production environments. The ideal candidate will have hands-on exposure across the ML lifecycle, including data pipelines, model deployment, and monitoring, along with familiarity with cloud and ML Ops practices. This role involves working closely with senior engineers and data scientists to operationalize ML models for use cases such as personalization, recommendations, and NLP, while contributing to scalable and reliable ML solutions.

• Assist in designing, developing, and maintaining ML pipelines covering data ingestion, preprocessing, model training, and deployment. Support deployment and scaling of ML models on cloud platforms such as AWS (SageMaker, EKS, Lambda) or GCP (Vertex AI, GKE, Cloud Functions) under guidance from senior team members. Contribute to building and maintaining CI/CD pipelines using tools like GitHub Actions or Jenkins for automated testing and deployment of ML workflows.
• Work on containerizing applications using Docker and assist with orchestration using Kubernetes, along with supporting infrastructure setup through Terraform or CloudFormation. Participate in implementing model lifecycle components such as model registries, feature stores (MLflow, Feast), and monitoring systems using tools like Prometheus and Grafana.
• Support the tracking of ML performance metrics, data drift, and model drift, and assist in maintaining model health and monitoring systems. Develop and maintain data pipelines using tools like Airflow, Spark, and SQL, and work with orchestration tools such as Apache Airflow or AWS Step Functions. Collaborate with data scientists to help productionize ML models and ensure smooth deployment into production systems, while contributing to debugging, testing, and improving existing pipelines.

• 2–4 years of experience in ML Engineering, Data Engineering, or MLOps, with exposure to end-to-end ML workflows. Proficiency in Python and SQL, along with hands-on experience or familiarity with ML frameworks such as Scikit-learn, TensorFlow, or PyTorch. Good understanding of machine learning concepts, evaluation techniques, and performance metrics, along with awareness of model monitoring, data drift, and model drift concepts.
• Experience or working knowledge of cloud platforms (AWS or GCP), CI/CD tools (GitHub Actions, Jenkins), containerization (Docker), and orchestration (Kubernetes). Familiarity with MLflow, Feast, Airflow, and monitoring tools like Prometheus or Grafana is preferred.
• Strong problem-solving skills, willingness to learn, and ability to work in collaborative team environments. Bachelor’s degree in computer science, Engineering, or a related discipline preferred.

Nice-to-have: Exposure to real-time ML serving (KFServing, Seldon, Ray Serve), A/B testing, or recommender systems. Understanding of experiment design or causal inference, and experience in media or subscription domains, will be an advantage.