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

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  Design, build, and deploy production-grade ML pipelines using modern frameworks and MLOps tools.
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  Develop and manage CI/CD pipelines for ML model deployment and monitoring.
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  Implement containerization (Docker) and orchestration (Kubernetes) for scalable model serving.
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  Collaborate with data scientists, data engineers, and architects to productionize ML models.
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  Ensure compliance with best practices for cloud-based ML deployments across AWS, Azure, or GCP.
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  Integrate third-party services and APIs for enhanced solution capabilities.
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  Contribute to architecture design while driving low-level implementation.
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  Work closely with cross-functional teams across geographies to deliver end-to-end AI/ML solutions.

Essential Skills / Experience

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  Hands-on experience in Generative AI and MLOps.
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  Strong proficiency in Python and ML frameworks such as TensorFlow, Keras, or PyTorch.
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  Experience with MLOps tools: MLFlow, Kubeflow, Weights & Biases, AWS SageMaker, Vertex AI, DVC, Airflow, Prefect.
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  Proven experience in CI/CD pipelines, version control systems (Git), and deployment automation (Jenkins, Cloud Build, etc.).
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  Strong knowledge of cloud platforms: AWS, GCP, Azure.
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  Proficiency in containerization (Docker), Kubernetes, and Kafka.
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  Strong background in statistical modeling, machine learning, and unstructured data analytics.
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  Deep understanding of ML lifecycle and hands-on experience in productionizing ML models.
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  Experience in data engineering pipelines.

Desirable Skills / Experience

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  Exposure to third-party integrations for AI/ML systems.
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  Experience in architecture evolution for large-scale AI/ML solutions.
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  Ability to work both independently and collaboratively in distributed teams.
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  Strong problem-solving, stakeholder management, and technical communication skills.