What are the responsibilities and job description for the AIML Engineer position at Jobs via Dice?
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AI/ML Engineer
Key Responsibilities 1. Model Development & Training Assist in developing, training, and validating ML/AI models. Experiment with algorithms for classification, regression, clustering, NLP, or CV. Perform hyperparameter tuning and evaluate model performance. 2. Data Handling Collect, clean, preprocess, and transform datasets. Work with structured/unstructured data (text, images, logs, etc.). Implement feature extraction and engineering. 3. AI/ML Engineering Support building model pipelines and MLOps workflows. Deploy models via APIs, containers, or cloud environments (Azure, AWS, Google Cloud Platform). Optimize models for performance, scalability, and cost. 4. AI Application Development Contribute to integrating AI models into applications or backend systems. Work with LLMs, embeddings, vector databases, or prompt engineering where applicable. 5. Research & Prototyping Explore new AI techniques, tools, frameworks. Build prototypes or proof-of-concept models. Document findings, experiments, and model metrics.
AI/ML Engineer
Key Responsibilities 1. Model Development & Training Assist in developing, training, and validating ML/AI models. Experiment with algorithms for classification, regression, clustering, NLP, or CV. Perform hyperparameter tuning and evaluate model performance. 2. Data Handling Collect, clean, preprocess, and transform datasets. Work with structured/unstructured data (text, images, logs, etc.). Implement feature extraction and engineering. 3. AI/ML Engineering Support building model pipelines and MLOps workflows. Deploy models via APIs, containers, or cloud environments (Azure, AWS, Google Cloud Platform). Optimize models for performance, scalability, and cost. 4. AI Application Development Contribute to integrating AI models into applications or backend systems. Work with LLMs, embeddings, vector databases, or prompt engineering where applicable. 5. Research & Prototyping Explore new AI techniques, tools, frameworks. Build prototypes or proof-of-concept models. Document findings, experiments, and model metrics.