AI/MLOps Engineer

Experience: Relevant experience in AI/ML engineering and MLOps

Key Responsibilities

• Design, develop, and deploy AI/ML models and solutions.
• Build and maintain scalable MLOps/AI pipelines for model training, deployment, monitoring, and lifecycle management.
• Develop production-ready AI applications using Python.
• Work with machine learning frameworks, APIs, cloud platforms, and modern AI/LLM technologies.
• Implement CI/CD, model versioning, experiment tracking, and automated deployment workflows.
• Monitor model performance, data quality, and system reliability in production.
• Collaborate with data scientists, software engineers, and other stakeholders to deliver end-to-end AI solutions.

Required Skills

• Strong Python programming skills.
• Solid understanding of Machine Learning and AI concepts.
• Hands-on experience with MLOps practices and tools.
• Experience building and deploying ML/AI models in production environments.
• Knowledge of Docker, Git, CI/CD, and cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Familiarity with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Good understanding of APIs, data pipelines, model monitoring, and scalable systems.
• Strong problem-solving and communication skills.

Preferred Skills

• Experience with Generative AI, LLMs, RAG, and AI agents.
• Familiarity with Kubernetes and ML orchestration tools.
• Experience with platforms/tools such as MLflow, Airflow, Kubeflow, or similar.
• Knowledge of vector databases and modern AI application architectures.