Must possess experience or interest in training and deploying machine learning models end to end
Good understanding of basic statistics and deep learning techniques
Prior experience in data integration, profiling, validation, and cleansing
Proficiency with SQL
Strong understanding of agile methodologies
Strong understanding of test-driven development
Comfortable using version control and enterprise task management tools .
Building data pipelines and architecture
Should have extensive experience with relational and NO-SQL databases
Should have proficiency in handling both structured and unstructured data sources
Exposure to distributed data processing platforms like Spark .
GenAI:
Experience with AI and Machine Learning algorithms.
Familiarity with AI platforms and understanding of their capabilities and limitations.
Experience with Natural Language Processing (NLP).
Understanding of RAG, neural networks, and other AI/ML concepts.
Ability to design and implement AI models.
Experience with AI/ML frameworks like TensorFlow, PyTorch, etc.
resent complex concepts to non-technical stakeholders.
MLOps Engineer :
Design, implement, and maintain end-to-end ML pipelines for model training, evaluation, and deployment
Collaborate with data scientists and software engineers to operationalize ML models, serving frameworks (TensorFlow Serving, TorchServe) and experience with MLOps tools
Develop and maintain CI/CD pipelines for ML workflows
Implement monitoring and logging solutions for ML models, experience with ML model serving frameworks (TensorFlow Serving, TorchServe)
Optimize ML infrastructure for performance, scalability, and cost-efficiency
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Data Science & Machine Learning - OtherEmployement Type: Full time