Machine Learning Solution Development Design develop and implement advanced machine learning models supervised and unsupervised to solve complex IT Operations problems including Event Correlation Anomaly Detection Root Cause Analysis Predictive Analytics and Auto-Remediation Leverage structured and unstructured datasets performing extensive feature engineering and data preprocessing to optimize model performance Apply strong statistical modeling hypothesis testing and experimental design principles to ensure rigorous model validation and reliable insights AI ML Product Platform Development Lead the end-to-end development of Data Science products from conceptualization and prototyping to deployment and maintenance Develop and deploy AI Agents for automating workflows in IT operations particularly within Networks and CyberSecurity domains Implement RAG Retrieval Augmented Generation based retrieval frameworks for state-of-the-art models to enhance contextual understanding and response generation Adopt AI to detect and redact sensitive data in logs and implement central data tagging for all logs to improve AI Model performance and governance MLOps Deployment Drive the operationalization of machine learning models through robust MLOps LLMOps practices ensuring scalability reliability and maintainability Implement models as a service via APIs utilizing containerization technologies Docker Kubernetes for efficient deployment and management Design build and automate resilient Data Pipelines in cloud environments GCP Azure using AI Agents and relevant cloud services Cloud DevOps Integration Integrate data science solutions with existing IT infrastructure and AIOps platforms e g IBM Cloud Paks Moogsoft BigPanda Dynatrace Enable and optimize AIOps features within Data Analytics tools Monitoring tools or dedicated AIOps platforms Champion DevOps practices including CI CD pipelines Jenkins GitLab CI GitHub Actions infrastructure-as-code Terraform Ansible CloudFormation and automation to streamline development and deployment workflows Performance Reliability Monitor and optimize platform performance ensuring systems are running efficiently and meeting defined Service Level Agreements SLAs Lead incident management efforts related to data science systems and implement continuous improvements to enhance reliability and resilience Leadership Collaboration Translate complex business problems into data science solutions understanding their strategic implications and potential business value Collaborate effectively with cross-functional teams including engineering product management and operations to define project scope requirements and success metrics Mentor junior data scientists and engineers fostering a culture of technical excellence continuous learning and innovation Clearly articulate complex technical concepts findings and recommendations to both technical and non-technical audiences influencing decision-making and driving actionable outcomes Best Practices Uphold best engineering practices including rigorous code reviews comprehensive testing and thorough documentation Maintain a strong focus on building maintainable scalable and secure systems Soft Skills Exceptional analytical and problem-solving skills with a track record of tackling ambiguous and complex challenges independently Robust communication and presentation skills with the ability to articulate complex technical concepts and findings to diverse audiences and influence stakeholders Ability to take end-to-end ownership of data science projects Commitment to best engineering practices including code reviews testing and documentation A strong desire to stay current with the latest advancements in AI ML and cloud technologies Education Bachelors or Master s in Computer Science Data Science Artificial Intelligence Machine Learning Statistics or a related quantitative field Experience 8 years of IT and 5 yrs of progressive experience as a Data Scientist with a significant focus on applying ML AI in IT Operations AIOps or a related domain Proven track record of building and deploying machine learning models into production environments Demonstrated experience with MLOps LLMOps principles and tools Experience with designing and implementing microservices and serverless architectures Hands-on experience with containerization technologies Docker Kubernetes Technical Skills Programming Proficiency in at least one major programming language preferably Python sufficient to effectively communicate with and guide engineering teams Java is also a plus Machine Learning Strong theoretical and practical understanding of various ML algorithms e g classification regression clustering time-series analysis deep learning and their application to IT operational data Cloud Platforms - Expertise with Google Cloud Platform GCP services is highly preferred including Dataflow Pub Sub Cloud Logging Compute Engine Kubernetes Engine Cloud Functions BigQuery Cloud Storage and Vertex AI - Experience with other major cloud providers AWS Azure is also valuable DevOps Tools - Experience with CI CD pipelines e g Jenkins GitLab CI GitHub Actions - Familiarity with infrastructure-as-code tools e g Terraform Ansible CloudFormation AIOps Observability - Knowledge of AIOps platforms such as IBM Cloud Paks Moogsoft BigPanda Dynatrace etc - Experience with log analytics platforms and data tagging strategies

📌 Ai Data Scientist (Tamil Nadu)
🏢 Ford Motor
📍 Tamil Nadu