We are looking for an experienced AI / MLOps Engineer to help design, deploy and operate production-grade AI platforms and machine learning solutions.
If you are passionate about Machine Learning, Cloud technologies, automation, and modern AI operations, we'd love to hear from you.
Key Responsibilities
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Design and implement scalable ML pipelines for training, deployment, and monitoring
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Automate data ingestion, preprocessing, and feature engineering workflows
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Integrate AI/ML models into enterprise applications and APIs
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Develop and maintain CI/CD pipelines for Machine Learning systems
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Monitor model performance, detect model drift, and automate retraining processes
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Deploy AI workloads using Docker and Kubernetes
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Ensure security, compliance, and operational excellence across AI services
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Collaborate closely with Data Scientists, DevOps Engineers, Software Developers, and business stakeholders
Required Skills
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3+ years of experience in AI/ML Engineering, MLOps, Machine Learning Engineering, or related roles
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Strong Python programming skills
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Experience deploying ML models into production environments
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Hands-on experience with Azure, AWS, or GCP
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Solid knowledge of Docker, Kubernetes, and CI/CD practices
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Experience with monitoring and observability tools
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Understanding of software engineering best practices and infrastructure automation
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Experience working in Agile environments
Technologies We Use
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Python
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TensorFlow
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PyTorch
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Scikit-Learn
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MLflow
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Kubeflow
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Apache Airflow
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Docker
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Kubernetes
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Microsoft Azure
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AWS
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Google Cloud Platform (GCP)
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Apache Spark
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Apache Kafka
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Terraform
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Prometheus
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Grafana
Nice to Have
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Experience with Generative AI (GenAI), Large Language Models (LLMs), or Agentic AI solutions
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AI, Machine Learning, Cloud, or Kubernetes certifications
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Experience working in large-scale enterprise environments
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Knowledge of Responsible AI, MLOps governance, and model lifecycle management
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