GLO AI-ML specialist

hpe `

Bengaluru 3 Years Exp Posted 15d ago

Responsibilities:

Applies basic knowledge of the client's business need to formulate and define analytic objectives. Uses available data elements, defines business rules, and solution objectives.
Develops, enhances and maintains a client's metadata based on analytic objectives. May load data into the infrastructure, creates hypothesis matrix, and identifies available data to prepare for the Exploratory Data Anlysis (EDA) and hypotheses.
Builds models to supports/contribute to the overall solution, validates initial model and validates results & performance after the implementation.
Researches, identifies, and aids in delivering data science solutions to problem domain. Contributes significantly in measurement of business performance based on the model deployed. If needed, leads the model enhancements.
Create visualization of the model's insights for easy consumption.

Design, develop, train, evaluate, and deploy machine learning models for real-world business problems.
Implement supervised, unsupervised, reinforcement learning, and deep learning algorithms.
Perform feature engineering, feature selection, model tuning, and performance optimization.
Develop predictive, classification, forecasting, recommendation, anomaly detection, and optimization models.
Conduct model validation, statistical analysis, and performance benchmarking.

Develop and deploy LLM-based applications using Generative AI technologies.
Build Retrieval Augmented Generation (RAG) solutions using vector databases and embeddings.
Design prompt engineering frameworks and AI agents to automate business processes.
Fine-tune and optimize foundation models for domain-specific use cases.

Develop deep learning solutions using TensorFlow, PyTorch, and related frameworks.
Build Natural Language Processing (NLP) solutions including document intelligence, summarization, classification, sentiment analysis, semantic search, and conversational AI.
Apply transformer architectures, embeddings, and modern NLP techniques for advanced AI applications.

Deploy machine learning models into production environments.
Implement model lifecycle management, model monitoring, automated retraining, and drift detection.
Build CI/CD pipelines for machine learning deployment and version control.
Ensure scalability, performance, reliability, and governance of ML systems.

Analyze large-scale structured and unstructured datasets.
Develop data preparation, feature extraction, and transformation frameworks.
Apply statistical modeling and experimental techniques to solve business challenges.
Design A/B testing and model evaluation strategies.

Stay current with emerging AI, Machine Learning, and Generative AI technologies.
Evaluate new algorithms, frameworks, and techniques to improve model performance.
- Drive innovation by identifying opportunities to leverage AI across business functions.