Senior Engineering Consultant-AI Science
Verizon
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Job summary
Verizon is seeking a Senior Engineer Consultant – AI Science to lead architecture and strategy for enterprise AI initiatives. You will design scalable AI/ML systems, integrate top-tier LLM APIs, and drive production deployment of AI models.
You will act as a trusted advisor to cross-functional teams and clients, translating business problems into numerical solutions while guiding best practices in AI safety, governance, and collaboration.
Qualifications
- Fluent in Python, SQL, and DL frameworks (PyTorch, TensorFlow).
- Hands-on with orchestration frameworks (LangChain, LlamaIndex, LangGraph).
- Experience with vector databases (Pinecone, Qdrant, Milvus).
- Cloud AI platforms and MLOps tooling for production AI.
Responsibilities
- Design scalable AI/ML architectures for enterprise systems.
- Programmatically integrate LLM APIs into workflows.
- Develop, train, and validate ML pipelines in production.
- Mentor engineers on modern AI development practices.
Skills
Education
Tools
Job description
Job ID: R-1102007
You want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere & always.
The Senior Engineer Consultant – AI Science serves as a technical leader and subject matter expert in artificial intelligence, machine learning, and advanced analytics. In this hybrid consulting and engineering role, you will bridge the gap between complex business challenges and cutting‑edge AI technologies. You will architect, build, and deploy production‑grade AI solutions—including Generative AI, Retrieval‑Augmented Generation (RAG), and autonomous Agentic AI systems to drive measurable business impact and transformation.
Design scalable, enterprise‑grade architecture for AI/ML systems, Generative AI models, and autonomous multi‑agent frameworks (e.g., LangGraph, CrewAI, AutoGen).
Define standards for Model Context Protocol (MCP), RAG architectures, prompt engineering, and LLM fine‑tuning/evaluation.
Assess technical feasibility, select model architectures, and define data pipelines for complex, structured, and unstructured datasets.
Programmatically integrate state‑of‑the‑art LLM APIs (e.g., OpenAI, Anthropic Claude, Google Gemini) into enterprise workflows.
Develop, train, and validate statistical models, optimization algorithms, and machine learning pipelines using Python, R, or C++.
Implement MLOps, AI observability, and tracing tools to monitor model drift, token usage efficiency, latency, and system performance in production.
Act as a trusted technical advisor to cross‑functional leaders, product managers, and external clients.
Translate unstructured business problems into actionable mathematical and computational models.
Communicate complex technical concepts, model outputs, and risk assessments to both technical teams and non‑technical business executives.
Champion enterprise AI safety, ethical AI, data governance, and secure coding practices.
Mentor junior AI engineers and data scientists on modern development workflows, including the effective leverage of AI coding assistants (e.g., GitHub Copilot, Cursor).
Languages & Frameworks: Fluent in Python, SQL, and Deep Learning frameworks (PyTorch, TensorFlow).
Generative AI & Agentic AI: Hands‑on experience with orchestration frameworks (LangChain, LlamaIndex, LangGraph), vector databases (Pinecone, Qdrant, Milvus), and agentic workflows.
Cloud & Infrastructure: Cloud AI platforms (AWS Bedrock/SageMaker, GCP Vertex AI, or Azure OpenAI/AI Foundry).
MLOps & Deployment: Experience with Docker, Kubernetes, CI/CD for AI models, MLflow, and API integration (REST, gRPC).
Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Artificial Intelligence, Applied Mathematics, or a related quantitative field.
Experience: 6+ years of relevant experience in software engineering, data science, or AI consulting, with a proven track record of shipping AI models into production environments.
Strong analytical reasoning and structured problem‑solving abilities.
Exceptional written and verbal communication skills for technical and business audiences.
Ability to work autonomously in ambiguity and manage multiple project streams simultaneously.
Experience with Model Context Protocol (MCP) and multi‑agent orchestration frameworks.
Industry expertise in supply chain, finance, telecommunications, or operations research.
Contributions to open‑source AI/ML libraries, technical publications, or patent filings.
In this hybrid role, you'll have a defined work location that includes work from home and assigned office days set by your manager.
40
Verizon is an equal opportunity employer. We evaluate qualified applicants without regard to race, gender, disability or any other legally protected characteristics.
You want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere & always.
The Senior Engineer Consultant – AI Science serves as a technical leader and subject matter expert in artificial intelligence, machine learning, and advanced analytics. In this hybrid consulting and engineering role, you will bridge the gap between complex business challenges and cutting‑edge AI technologies. You will architect, build, and deploy production‑grade AI solutions—including Generative AI, Retrieval‑Augmented Generation (RAG), and autonomous Agentic AI systems to drive measurable business impact and transformation.
Design scalable, enterprise‑grade architecture for AI/ML systems, Generative AI models, and autonomous multi‑agent frameworks (e.g., LangGraph, CrewAI, AutoGen).
Define standards for Model Context Protocol (MCP), RAG architectures, prompt engineering, and LLM fine‑tuning/evaluation.
Assess technical feasibility, select model architecture, and define data pipelines for complex, structured, and unstructured datasets.
Programmatically integrate state‑of‑the‑art LLM APIs (e.g., OpenAI, Anthropic Claude, Google Gemini) into enterprise workflows.
Develop, train, and validate statistical models, optimization algorithms, and machine learning pipelines using Python, R, or C++.
Implement MLOps, AI observability, and tracing tools to monitor model drift, token usage efficiency, latency, and system performance in production.
Act as a trusted technical advisor to cross‑functional leaders, product managers, and external clients.
Translate unstructured business problems into actionable mathematical and computational models.
Communicate complex technical concepts, model outputs, and risk assessments to both technical teams and non‑technical business executives.
Champion enterprise AI safety, ethical AI, data governance, and secure coding practices.
Mentor junior AI engineers and data scientists on modern development workflows, including the effective leverage of AI coding assistants (e.g., GitHub Copilot, Cursor).
Languages & Frameworks: Fluent in Python, SQL, and Deep Learning frameworks (PyTorch, TensorFlow).
Generative AI & Agentic AI: Hands‑on experience with orchestration frameworks (LangChain, LlamaIndex, LangGraph), vector databases (Pinecone, Qdrant, Milvus), and agentic workflows.
Cloud & Infrastructure: Cloud AI platforms (AWS Bedrock/SageMaker, GCP Vertex AI, or Azure OpenAI/AI Foundry).
MLOps and deployment: Experience with Docker, Kubernetes, CI/CD for AI models, MLflow, and API integration (REST, gRPC).
Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Artificial Intelligence, Applied Mathematics, or a related quantitative field.
Experience: 6+ years of relevant experience in software engineering, data science, or AI consulting, with a proven track record of shipping AI models into production environments.
Strong analytical reasoning and structured problem‑solving abilities.
Exceptional written and verbal communication skills for technical and business audiences.
Ability to work autonomously in ambiguity and manage multiple project streams simultaneously.
Experience with Model Context Protocol (MCP) and multi‑agent orchestration frameworks.
Industry expertise in supply chain, finance, telecommunications, or operations research.
Contributions to open‑source AI/ML libraries, technical publications, or patent filings.
In this hybrid role, you'll have a defined work location that includes work from home and assigned office days set by your manager.
40
Verizon is an equal opportunity employer. We evaluate qualified applicants without regard to race, gender, disability or any other legally protected characteristics.
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