AI / ML Operations Lead

Your Experience and Qualifications

• Min 8+ years of experience in data, with at least 5+ years of experience in AI, machine learning, or data science, with a focus on operationalizing AI solutions in a cloud environment.
• Strong proficiency in programming languages such as Python, R, and SQL and big data tools such as Hive, Spark, Hadoop, etc.
• Experience with cloud platforms (e.g., AWS, Google Cloud, Databricks) and deploying AI models in production environments, including services like AWS (SageMaker, Redshift, Lambda), Google Cloud Platform (BigQuery, Vertex AI) and AI / ML automation tools.
• Experience working with deep learning algorithms and large datasets including knowledge of generative AI models and various ML algorithms like RAGs, LLMs, NLPs, RNNs, CNNs, ARIMA, Neural Networks, etc.
• Experience in data mining and predictive modeling inclusive of linear and non-linear regression, logistic regression, and time series analysis models.
• Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, TensorFlow, PyTorch, Scikit-learn, VertexAI, SageMaker) for model tracking, versioning, and deployment.
• Experience with ETL processes including knowledge about web scraping, real-time data streaming, etc. and familiarity with BI tools, particularly Tableau.
• Skilled in Docker, Kubernetes, and other container management tools to ensure scalability and efficiency in model deployment and orchestration.
• Familiar with data governance frameworks and security protocols, including encryption, authentication, and compliance requirements (GDPR, CPRA, etc.), especially as they relate to cloud-based AI solutions.
• Experience in industries such as manufacturing, agriculture, or supply chain, particularly in AI and data use cases is a plus.

Skills Required

Aws, Google Cloud, Databricks, Spark, Hadoop