AI/ML Engineer (Saidapet)

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. - Solid 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. .