AI/ML Engineer

Introduction to the Role We are seeking an experienced MLOps / GenAI Engineer with strong expertise in building and deploying production-grade ML pipelines, cloud-native solutions, and MLOps frameworks. The role requires a deep understanding of the ML lifecycle, CI/CD automation, containerization, and orchestration to deliver scalable, secure, and high-performing AI/ML solutions in enterprise environments. Accountabilities • Design, build, and deploy production-grade ML pipelines using modern frameworks and MLOps tools. • Develop and manage CI/CD pipelines for ML model deployment and monitoring. • Implement containerization (Docker) and orchestration (Kubernetes) for scalable model serving. • Collaborate with data scientists, data engineers, and architects to productionize ML models. • Ensure compliance with best practices for cloud-based ML deployments across AWS, Azure, or GCP. • Integrate third-party services and APIs for enhanced solution capabilities. • Contribute to architecture design while driving low-level implementation. • Work closely with cross-functional teams across geographies to deliver end-to-end AI/ML solutions. Essential Skills / Experience • Hands-on experience in Generative AI and MLOps. • Strong proficiency in Python and ML frameworks such as TensorFlow, Keras, or PyTorch. • Experience with MLOps tools: MLFlow, Kubeflow, Weights & Biases, AWS SageMaker, Vertex AI, DVC, Airflow, Prefect. • Proven experience in CI/CD pipelines, version control systems (Git), and deployment automation (Jenkins, Cloud Build, etc.). • Strong knowledge of cloud platforms: AWS, GCP, Azure. • Proficiency in containerization (Docker), Kubernetes, and Kafka. • Strong background in statistical modeling, machine learning, and unstructured data analytics. • Deep understanding of ML lifecycle and hands-on experience in productionizing ML models. • Experience in data engineering pipelines. Desirable Skills / Experience • Exposure to third-party integrations for AI/ML systems. • Experience in architecture evolution for large-scale AI/ML solutions. • Ability to work both independently and collaboratively in distributed teams. • Strong problem-solving, stakeholder management, and technical communication skills.