MLOps Engineer

We are seeking a skilled  ML Ops Engineer  to join our team and streamline the deployment, monitoring, and management of machine learning models at scale. You will collaborate closely with data scientists, engineers, and DevOps to build robust, scalable ML infrastructure and CI / CD pipelines for AI / ML workflows.

ML Ops engineer / ML engineer

Project Description

The engineer is supposed to participate in various AI projects such as Demand Sensing and Forecasting, Price and Promotion Optimization and others.

Details on Tech Stack

• Proficiency in Python.
• Competent knowledge of best practices for software development.
• Strong understanding of Data Science concepts such as supervised and unsupervised learning, feature engineering and ETL processes, classical DS models types and neural networks types, hyperparameters tuning, model evaluation and selection.
• Proficiency in usage of appropriate cloud services (AWS / GCP / Azure) for building end-to-end ML pipelines, e.g. GCP Vertex AI, BigQuery, Dataflow, Cloud SQL, Dataproc, Cloud Functions, Google Kubernetes Engine.
• Competent knowledge of MLOps paradigm and practices. Experience with MLOps tools (or appropriate cloud services), including model and data versioning and experiment tracking (e.g., DVC, MLflow, Weights & Biases), pipeline orchestration (e.g., Apache Airflow, Kubeflow, Dagster). Understanding of deployment strategies for different types of models and inference (batch / online).
• Knowledge and experience with big data processing frameworks (e.g., Apache Spark, Apache Kafka, Apache Hadoop, Dask).
• Competent SQL skills and experience with RDBMS databases (like MySQL, Postgres)
• Experience in developing and integrating RESTful APIs for ML model serving (e.g., Flask and FastAPI).
• Experience with containerization technologies like Docker and orchestration tools (e.g., Kubernetes).

Nice to Have Requirements

Perks & Benefits :

Skills Required

Crew, MLops, Docker, Flow