29/08/2025 AI/ML Operations Lead

Job Description

- Monitor and maintain AI/ML models in production to ensure optimal performance and reliability, addressing any technical challenges that arise.
- Collaborate with development teams to understand the AI use cases and their integration into production environments.
- Design and implement scalable architectures ensuring that productionalized AI use cases are at optimal performance, scalable, accurate, and secure, adhering to ethical AI/ML practices, compliance and governance standards.
- Conduct regular assessments of AI models to identify areas for improvement. Enhance and fine-tune models to generate high-quality outputs for various applications.
- Guide team members for implementation of robust data pipelines and workflows to support data science and AI initiatives.
- Collaborate with ML engineers to establish & maintain CI/CD pipelines for model deployment, monitoring, and lifecycle management to ensure models remain reliable and efficient in production.
- Develop and maintain documentation related to AI operations, including monitoring processes, performance metrics, and governance compliance.
- Work with cross-functional teams to integrate AI models into websites, Tableau dashboards, and other applications as necessary.
- Provide support and troubleshoot issues in production environments, identifies solutions and implements resolutions swiftly.
- Partner with AI & Advanced Analytics development team in maintaining AI tools in production and monitoring for accuracy and support. Performs exploratory data analysis to identify trends, patterns, and anomalies, etc.
- Stay up to date with industry trends and emerging technologies in AI and machine learning and recommend innovative solutions to improve our operational capabilities.
- Facilitate communication and collaboration between the AI & BI Ops team and other business units to align AI initiatives with organizational goals. Provide technical guidance and mentorship to team members on AI best practices, tools, and methodologies.

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.

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