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

• Knowledge of monitoring and logging tools (e.g., Grafana, ELK Stack or appropriate cloud services).
• Understanding of CI/CD principles and tools (e.g., Jenkins, GitLab CI) for automating the testing and deployment of machine learning models and applications.
• Experience with Cloud Identity and Access Management.
• Experience with Cloud Load Balancing.
• Knowledge of Infrastructure as Code (IaC) tools such as Terraform and Ansible.
• Experience with observability tools like Evidently, Arize, Weights & Biases

Perks & Benefits:

• Competitive salary & performance-based bonuses
• Remote work flexibility / Hybrid options
• Continuous learning budget & GenAI certifications
• Opportunity to work on cutting-edge AI projects
• Dynamic and collaborative team environment