Job
Description
Overview:
Senior Engineer Data Scientist - AI & Machine Learning
.
Responsibilities include:
End-to-End Model Development: Lead the entire lifecycle of machine learning projects, from data acquisition and feature engineering to model training, validation, deployment, and monitoring in a cloud environment.
Generative AI Innovation: Architect and implement advanced Generative AI solutions. This includes developing and fine-tuning Large Language Models (LLMs), building robust Retrieval-Augmented Generation (RAG) and Multi-Modal RAG pipelines to ground models in factual data.
AI System Evaluation: Design and implement comprehensive evaluation frameworks to measure the performance, fairness, and safety of LLMs and other generative models, ensuring they meet Verizon's high standards for reliability and accuracy.
Advanced Analytics & Insights: Perform deep-dive data analysis using advanced statistical methods to uncover actionable insights. Wrangle and query large, complex datasets from multiple sources using expert-level SQL.
AI Agent Development: Research, conceptualize, and contribute to the development of autonomous and semi-autonomous AI agents that can perform complex tasks and streamline business processes.
Cloud-Native AI: Leverage Google Cloud Platform (GCP) services (e.g., Vertex AI, BigQuery, GCS, Google Kubernetes Engine) to build scalable, production-grade AI/ML solutions.
Communication & Storytelling: Clearly articulate and present complex technical concepts and model results to both technical and non-technical audiences, influencing business strategy with data-driven narratives.
What we're looking for...
You are a deeply curious and highly skilled data scientist with a proven track record of delivering impactful AI/ML solutions. You possess a strong foundation in machine learning theory and an insatiable appetite for the latest advancements in Generative AI. You are a hands-on builder who thrives in a collaborative, fast-paced environment and is comfortable tackling ambiguous problems.
You'll need to have:
Bachelor’s degree in Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field.
Six or more years of professional experience in data science, machine learning, or a related role.
Expert-level hands-on proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit-learn, Matplotlib).
Deep theoretical and practical knowledge of a wide range of machine learning algorithms (e.g., classification, regression, clustering, ensemble methods) and deep learning.
Proven hands-on experience developing and deploying Generative AI solutions, with a strong understanding of LLMs, transformers, and vector databases.
Experience with Multi-Modal RAG systems (handling text, images, and other data types).
Strong proficiency in SQL for complex data extraction, manipulation, and analysis on large-scale datasets.
Hands-on experience with cloud computing platforms, specifically Google Cloud Platform (GCP) and its AI/ML services.
Even better if you have:
A Master’s or Ph.D. in a relevant quantitative field.
Practical experience in developing AI agents using frameworks like LangChain, Agent Development Kit (ADK), or LlamaIndex.
Experience in designing and implementing robust LLM evaluation metrics and frameworks (e.g., RAGAS, ARES, TruLens)
Experience with voice and speech technologies, such as Speech-to-Text (STT), Text-to-Speech (TTS), and building conversational AI or voicebot systems.
Experience with MLOps/LLMOps principles and tools (e.g., Docker, Kubernetes, CI/CD pipelines for models, MLflow).
GCP Professional Machine Learning Engineer certification.
Responsibilities:
Senior Engineer Data Scientist - AI & Machine Learning
.
Responsibilities include:
End-to-End Model Development: Lead the entire lifecycle of machine learning projects, from data acquisition and feature engineering to model training, validation, deployment, and monitoring in a cloud environment.
Generative AI Innovation: Architect and implement advanced Generative AI solutions. This includes developing and fine-tuning Large Language Models (LLMs), building robust Retrieval-Augmented Generation (RAG) and Multi-Modal RAG pipelines to ground models in factual data.
AI System Evaluation: Design and implement comprehensive evaluation frameworks to measure the performance, fairness, and safety of LLMs and other generative models, ensuring they meet Verizon's high standards for reliability and accuracy.
Advanced Analytics & Insights: Perform deep-dive data analysis using advanced statistical methods to uncover actionable insights. Wrangle and query large, complex datasets from multiple sources using expert-level SQL.
AI Agent Development: Research, conceptualize, and contribute to the development of autonomous and semi-autonomous AI agents that can perform complex tasks and streamline business processes.
Cloud-Native AI: Leverage Google Cloud Platform (GCP) services (e.g., Vertex AI, BigQuery, GCS, Google Kubernetes Engine) to build scalable, production-grade AI/ML solutions.
Communication & Storytelling: Clearly articulate and present complex technical concepts and model results to both technical and non-technical audiences, influencing business strategy with data-driven narratives.
What we're looking for...
You are a deeply curious and highly skilled data scientist with a proven track record of delivering impactful AI/ML solutions. You possess a strong foundation in machine learning theory and an insatiable appetite for the latest advancements in Generative AI. You are a hands-on builder who thrives in a collaborative, fast-paced environment and is comfortable tackling ambiguous problems.
You'll need to have:
Bachelor’s degree in Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field.
Six or more years of professional experience in data science, machine learning, or a related role.
Expert-level hands-on proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit-learn, Matplotlib).
Deep theoretical and practical knowledge of a wide range of machine learning algorithms (e.g., classification, regression, clustering, ensemble methods) and deep learning.
Proven hands-on experience developing and deploying Generative AI solutions, with a strong understanding of LLMs, transformers, and vector databases.
Experience with Multi-Modal RAG systems (handling text, images, and other data types).
Strong proficiency in SQL for complex data extraction, manipulation, and analysis on large-scale datasets.
Hands-on experience with cloud computing platforms, specifically Google Cloud Platform (GCP) and its AI/ML services.
Even better if you have:
A Master’s or Ph.D. in a relevant quantitative field.
Practical experience in developing AI agents using frameworks like LangChain, Agent Development Kit (ADK), or LlamaIndex.
Experience in designing and implementing robust LLM evaluation metrics and frameworks (e.g., RAGAS, ARES, TruLens)
Experience with voice and speech technologies, such as Speech-to-Text (STT), Text-to-Speech (TTS), and building conversational AI or voicebot systems.
Experience with MLOps/LLMOps principles and tools (e.g., Docker, Kubernetes, CI/CD pipelines for models, MLflow).
GCP Professional Machine Learning Engineer certification.
Requirements:
Senior Engineer Data Scientist - AI & Machine Learning
.
Responsibilities include:
End-to-End Model Development: Lead the entire lifecycle of machine learning projects, from data acquisition and feature engineering to model training, validation, deployment, and monitoring in a cloud environment.
Generative AI Innovation: Architect and implement advanced Generative AI solutions. This includes developing and fine-tuning Large Language Models (LLMs), building robust Retrieval-Augmented Generation (RAG) and Multi-Modal RAG pipelines to ground models in factual data.
AI System Evaluation: Design and implement comprehensive evaluation frameworks to measure the performance, fairness, and safety of LLMs and other generative models, ensuring they meet Verizon's high standards for reliability and accuracy.
Advanced Analytics & Insights: Perform deep-dive data analysis using advanced statistical methods to uncover actionable insights. Wrangle and query large, complex datasets from multiple sources using expert-level SQL.
AI Agent Development: Research, conceptualize, and contribute to the development of autonomous and semi-autonomous AI agents that can perform complex tasks and streamline business processes.
Cloud-Native AI: Leverage Google Cloud Platform (GCP) services (e.g., Vertex AI, BigQuery, GCS, Google Kubernetes Engine) to build scalable, production-grade AI/ML solutions.
Communication & Storytelling: Clearly articulate and present complex technical concepts and model results to both technical and non-technical audiences, influencing business strategy with data-driven narratives.
What we're looking for...
You are a deeply curious and highly skilled data scientist with a proven track record of delivering impactful AI/ML solutions. You possess a strong foundation in machine learning theory and an insatiable appetite for the latest advancements in Generative AI. You are a hands-on builder who thrives in a collaborative, fast-paced environment and is comfortable tackling ambiguous problems.
You'll need to have:
Bachelor’s degree in Computer Science, Statistics, Engineering, Mathematics, or a related quantitative field.
Six or more years of professional experience in data science, machine learning, or a related role.
Expert-level hands-on proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit-learn, Matplotlib).
Deep theoretical and practical knowledge of a wide range of machine learning algorithms (e.g., classification, regression, clustering, ensemble methods) and deep learning.
Proven hands-on experience developing and deploying Generative AI solutions, with a strong understanding of LLMs, transformers, and vector databases.
Experience with Multi-Modal RAG systems (handling text, images, and other data types).
Strong proficiency in SQL for complex data extraction, manipulation, and analysis on large-scale datasets.
Hands-on experience with cloud computing platforms, specifically Google Cloud Platform (GCP) and its AI/ML services.
Even better if you have:
A Master’s or Ph.D. in a relevant quantitative field.
Practical experience in developing AI agents using frameworks like LangChain, Agent Development Kit (ADK), or LlamaIndex.
Experience in designing and implementing robust LLM evaluation metrics and frameworks (e.g., RAGAS, ARES, TruLens)
Experience with voice and speech technologies, such as Speech-to-Text (STT), Text-to-Speech (TTS), and building conversational AI or voicebot systems.
Experience with MLOps/LLMOps principles and tools (e.g., Docker, Kubernetes, CI/CD pipelines for models, MLflow).
GCP Professional Machine Learning Engineer certification.
