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.