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Design, develop, and deploy machine learning and AI models for business applications; Build and optimize data pipelines for model training, validation, and inference; Implement algorithms using Python and ML/DL frameworks (TensorFlow, PyTorch, Scikit learn); Integrate AI models into applications using REST APIs, microservices, or cloud services; Perform model evaluation, tuning, and performance optimization; Monitor models in production for accuracy, drift, and reliability; Collaborate with product managers, data scientists, and engineers to translate requirements into AI solutions; Implement MLOps practices including versioning, CI/CD, and automated deployments; Ensure adherence to Responsible AI, security, data privacy, and compliance standards; Document models, pipelines, and deployment procedures
Job Responsibility:
Design, develop, and deploy machine learning and AI models for business applications
Build and optimize data pipelines for model training, validation, and inference
Implement algorithms using Python and ML/DL frameworks (TensorFlow, PyTorch, Scikit learn)
Integrate AI models into applications using REST APIs, microservices, or cloud services
Perform model evaluation, tuning, and performance optimization
Monitor models in production for accuracy, drift, and reliability
Collaborate with product managers, data scientists, and engineers to translate requirements into AI solutions
Implement MLOps practices including versioning, CI/CD, and automated deployments
Ensure adherence to Responsible AI, security, data privacy, and compliance standards
Document models, pipelines, and deployment procedures
Requirements:
Strong programming skills in Python (mandatory)
Solid understanding of machine learning algorithms and statistics
Hands on experience with ML/DL frameworks (TensorFlow, PyTorch, Scikit learn)
Experience working with structured and unstructured data
Knowledge of SQL and data processing libraries (Pandas, NumPy)
Experience deploying models using APIs, Docker, and cloud platforms (AWS/Azure/GCP)
Familiarity with Git, CI/CD pipelines, and software engineering best practices
Nice to have:
Experience with Generative AI / LLMs (OpenAI, Azure OpenAI, Hugging Face)
Knowledge of MLOps tools (MLflow, Kubeflow, Airflow)
Experience with big data technologies (Spark, Databricks)