Senior ai/ml engineer (id: 3865)

• Design, develop, and deploy scalable AI, Machine Learning, and Generative AI solutions for enterprise applications.
• Build intelligent automation solutions, predictive analytics models, incident prediction systems, root cause analysis engines, and AI-powered knowledge assistants.
• Develop production-grade Python applications, APIs, and microservices to expose AI capabilities across enterprise platforms.
• Design and implement robust data ingestion, preprocessing, feature engineering, and transformation pipelines for large-scale datasets.
• Integrate AI/ML models into Azure DevOps CI/CD pipelines, enabling automated model building, testing, deployment, and monitoring.
• Build and maintain MLOps pipelines to support continuous model training, validation, deployment, versioning, and lifecycle management.
• Collaborate with DevOps, platform engineering, data engineering, and product teams to deliver reliable, scalable AI solutions.
• Optimize AI models for accuracy, scalability, performance, and operational efficiency within enterprise environments.
• Ensure AI solutions comply with enterprise governance, security, audit, and regulatory standards.
• Participate in architecture discussions, technical design reviews, and continuous improvement initiatives for AI platforms., * Design and maintain production-ready MLOps pipelines supporting continuous delivery of AI models.
• Translate complex business challenges into practical AI-driven solutions.
• Collaborate effectively with business stakeholders, architects, DevOps engineers, and data teams.
• Monitor, troubleshoot, and continuously improve AI model performance in production.
• Ensure enterprise-grade governance, security, compliance, and operational excellence throughout the AI lifecycle.

What We Bring to the Table:

• Opportunity to work on enterprise-scale AI, Machine Learning, and Generative AI transformation initiatives.
• Exposure to modern cloud-native architectures, MLOps platforms, and Azure DevOps ecosystems.
• Collaborative environment involving AI engineers, cloud architects, DevOps specialists, and business stakeholders.
• Challenging projects focused on intelligent automation, predictive analytics, and enterprise AI innovation.
• Opportunities for continuous learning, technical leadership, and professional growth.
• A culture that values innovation, engineering excellence, and knowledge sharing.

• 6-8 years of experience in Python development, AI/ML engineering, or enterprise software development.
• Strong hands-on expertise in Python for developing scalable backend applications and AI solutions.
• Experience designing, training, validating, deploying, and optimizing Machine Learning and Deep Learning models.
• Practical experience building Generative AI applications, AI Agents, intelligent automation solutions, or conversational AI systems.
• Strong knowledge of Azure cloud services and Azure DevOps for enterprise application delivery.
• Experience implementing CI/CD pipelines and MLOps workflows for AI model deployment and lifecycle management.
• Strong understanding of data engineering concepts including data preprocessing, feature engineering, and pipeline orchestration.
• Experience developing RESTful APIs and microservices to expose AI capabilities.
• Familiarity with enterprise DevOps tools such as Azure DevOps, Nexus, Ansible, and related automation frameworks.
• Strong analytical, problem-solving, communication, and stakeholder management skills.

You Should Possess the Ability to:

• Design end-to-end AI solutions from concept through production deployment.
• Build scalable, maintainable, and secure Python applications for enterprise environments., As a Senior AI/ML Engineer, you will: Design, develop, and deploy scalable AI, Machine Learning, and Generative AI solutions for enterprise applications. Build intelligent automation solutions, predictive analytics models, incident prediction systems, root cause analysis engines, and AI-powered knowledge assistants. Develop production-grade Python applications, APIs, and microservices to expose AI capabilities across enterprise platforms. Design and implement robust data ingestion, preprocessing, feature engineering, and transformation pipelines for large-scale datasets. Integrate AI/ML models into Azure DevOps CI/CD pipelines, enabling automated model building, testing, deployment, and monitoring. Build and maintain MLOps pipelines to support continuous model training, validation, deployment, versioning, and lifecycle management. Collaborate with DevOps, platform engineering, data engineering, and product teams to deliver reliable, scalable AI solutions. Optimize AI models for accuracy, scalability, performance, and operational efficiency within enterprise environments. Ensure AI solutions comply with enterprise governance, security, audit, and regulatory standards. Participate in architecture discussions, technical design reviews, and continuous improvement initiatives for AI platforms. What You Bring to the Table: 6-8 years of experience in Python development, AI/ML engineering, or enterprise software development. Strong hands-on expertise in Python for developing scalable backend applications and AI solutions. Experience designing, training, validating, deploying, and optimizing Machine Learning and Deep Learning models. Practical experience building Generative AI applications, AI Agents, intelligent automation solutions, or conversational AI systems. Strong knowledge of Azure cloud services and Azure DevOps for enterprise application delivery. Experience implementing CI/CD pipelines and MLOps workflows for AI model deployment and lifecycle management. Strong understanding of data engineering concepts including data preprocessing, feature engineering, and pipeline orchestration. Experience developing RESTful APIs and microservices to expose AI capabilities. Familiarity with enterprise DevOps tools such as Azure DevOps, Nexus, Ansible, and related automation frameworks. Strong analytical, problem-solving, communication, and stakeholder management skills. You Should Possess the Ability to: Design end-to-end AI solutions from concept through production deployment. Build scalable, maintainable, and secure Python applications for enterprise environments. Integrate AI capabilities seamlessly into DevOps and CI/CD ecosystems. Design and maintain production-ready MLOps pipelines supporting continuous delivery of AI models. Translate complex business challenges into practical AI-driven solutions. Collaborate effectively with business stakeholders, architects, DevOps engineers, and data teams., Monitor, troubleshoot, and continuously improve