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Verizon
Dist Engr-AI Science
Verizon
Hyderabad•Hybrid•12+ Yrs•2 days ago
Job description
Hybrid
Overview
When you join Verizon
Responsibilities
- Design, develop, and deploy end-to-end AI/ML solutions, including data pipelines, model training, deployment, monitoring, and optimization.
- Develop technical high level and low level architecture for generative and agentic systems
- Lead and mentor a high performing team of data scientists and AI/ML engineers, fostering a culture of collaboration and innovation.
- Stay abreast of the latest advancements and trends in Generative/Agentic AI, continuously evaluating and integrating emerging technologies.
- Collaborate closely with cross-functional teams, including product, engineering, and business stakeholders, to translate business requirements into innovative AI/ML solutions.
- Champion the adoption of AI/ML best practices, ensuring the development and deployment of secure, reliable, and ethical AI systems.
Qualifications
- Master’s or Ph.D. degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- At least 12+ years of experience in architecting, designing, and implementing large-scale AI/ML systems in a production environment, including implementation of large-scale NLP Projects and Fine tuning & Evaluation of LLMs for downstream tasks such as text generation, Classification, summarization, question answering, entity extraction etc.
- Proven track record of designing, developing and deploying agentic solutions at scale with measurable results mapped to business goals
- Ability to represent the team in leadership forums, and elevate the AI practice by creating next gen solutions and evangelizing best practices
- Ability to guide the team to correctly analyze cognitive insights and leverage unstructured conversational data to create transformative, intelligent, context aware and adaptive AI systems
- Working knowledge of NLP frameworks and libraries like NLTK, Spacy, Transformers, Pytorch, Tensorflow, hugging face api’s.
- Working knowledge of various supervised and unsupervised ML algorithms . Should know the various data preprocessing techniques and its impact on algorithm's accuracy, precision and recall.
- Knowledge & Implementation Experience of Deep Learning i.e Convolutional Neural Nets (CNN), Recursive Neural Nets (RNN) & Long Short Term Memory (LSTM), Generative Adversarial Networks (GAN), Deep Reinforcement Learning.
- Working knowledge on these: Word embeddings, TF-IDF, Tokenization, N-Grams, Stemmers, lemmatization, Part of speech tagging, entity resolution, ontology, lexicology, phonetics, intents, entities, and context.
- Expertise in Python, Sql, PySpark, Scala and/or other languages and tools.
- Understanding of validation framework for generative model output and perspective on future ready systems to scale validation
- Familiarity with GPU/CPU architecture and distributed computing and general infra needs to scale Gen AI models
- Ability to provide technical thought leadership on model architecture, delivery, monitoring, measurement and model lifecycle best practices.
- Excellent communication, interpersonal, and presentation skills, with the ability to effectively communicate complex technical concepts to both technical and non-technical audiences.
- Contributions to open-source AI/ML projects.
- Familiarity with telecommunications industry data and use cases.