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.
