ML Operations Engineer - Associate Vice President

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* ** ML Pipeline Development & Automation:
** Design, build, and maintain robust and scalable end-to-end ML pipelines for data ingestion, preprocessing, model training, validation, and deployment.
* ** CI/CD for ML:
** Implement and manage Continuous Integration/Continuous Delivery (CI/CD) pipelines specifically tailored for machine learning workflows, ensuring automated testing, versioning, and deployment of ML artifacts.
* ** Experiment Tracking & Model Management:
** Utilize
** MLflow
* * extensively for experiment tracking, reproducible runs, managing model versions, and maintaining a centralized model registry.
* ** Hyperparameter Optimization:
** Leverage
** Ray Tune
** for efficient and distributed hyperparameter optimization to enhance model performance and accelerate experimentation.
* ** Containerization & Orchestration:
** Package ML models and their dependencies using Docker and deploy/manage them effectively on Kubernetes clusters.
* ** Data Platform Integration:
** Integrate with and optimize existing data platforms, including Apache Iceberg, Apache Spark, and FLINK, to ensure efficient data processing and feature engineering for ML models.
* ** Data Storage & Streaming:
** Work with Postgre

SQL, Oracle, and Mongo

DB for diverse data storage needs, and utilize Kafka for real-time data streaming to support various ML applications.
* ** Monitoring & Observability:
** Implement comprehensive monitoring, logging, and alerting solutions (e.g., Prometheus, Grafana) for ML models in production, tracking model performance, data drift, and infrastructure health to ensure reliability and facilitate automated retraining or rollback.
* ** Scripting & Automation:
** Develop automation scripts and tools using
** Python
* * and Bash/Go to streamline MLOps processes and integrate various systems.
* *
* Collaboration:

** Act as a vital link between data scientists, ML engineers, and infrastructure teams, facilitating clear communication and ensuring that ML solutions are production-ready.
* *
* Experience:

** 3-5 years of hands-on experience in an MLOps, Dev Ops, or Machine Learning Engineering role, with a proven track record of deploying and managing ML models in production environments.
* ** Programming:
** Expert-level proficiency in
** Python
* * for ML development, scripting, and automation.
* ** MLOps Tooling:
** Demonstrated hands-on experience with
** Ray Tune
** for hyperparameter optimization and
** Air Flow or MLflow
** for experiment tracking and model management.
* ** Containerization & Orchestration:
** Strong experience with Docker and Kubernetes (including Helm).
* ** CI/CD:
** Experience implementing CI/CD practices for software and/or ML pipelines.
* ** Data Technologies:
** Familiarity with or experience with Apache Spark, Apache Iceberg, FLINK, and Kafka.
* ** Databases:
** Experience with Postgre

SQL, Oracle, and Mongo

DB.
* ** Workflow Orchestration:
** Experience with Apache Airflow.
* ** Infrastructure as Code:
** Experience with Hashi Corp (Terraform).
* ** Operating Systems:
** Proficiency in Linux/Unix environments.
* Experience with cloud platforms (AWS, Azure, GCP) and managing cloud-native ML infrastructure.
* Knowledge of deep learning frameworks such as Tensor Flow or PyTorch.
* Experience with generative AI technologies (e.g., LLMs, prompt engineering, RAG pipelines).
* Understanding of distributed computing and big data processing techniques.
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