Sr Engr Cslt-AI Science

When you join Verizon 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. Want in? Join the #VTeamLife. As a Senior Engineer Consultant - AI Science , you will architect, build, and deploy production-grade Generative AI, Agentic workflows, and Advanced NLP systems for enterprise clients. You will bridge the gap between applied research and enterprise engineering—translating ambiguous business requirements into robust, high-performance AI architectures. You will guide business from proof-of-concept (PoC) to scalable production, establish technical governance, and mentor emerging AI talent. What You Will Do Architect & Deliver End-to-End Solutions: Design, prototype, benchmark, and deploy enterprise-grade Agentic systems, multi-stage RAG pipelines, and fine-tuned Foundation/Small Language Model (LLM/SLM) architectures to solve complex business problems. Lead AI Engineering & Productionization: Implement modern LLMOps/GenAIOps standards, including prompt orchestration, deterministic guardrails, structured outputs, automated red-teaming, latency/token-cost optimization, and continuous evaluation (e.g., LLM-as-a-judge). Execute High-Impact Fine-Tuning & Quantization: Conduct parameter-efficient fine-tuning (PEFT/LoRA/QLoRA), alignment tuning (DPO, RLHF), and model quantization/distillation for downstream domain specialization and edge/on-premise inference. Drive Client Advisory & Executive Communication: Partner with cross-functional global stakeholders, translate complex algorithmic decisions into measurable business impact (ROI, cost-per-query, latency budgets), and deliver compelling technical presentations to C-suite leadership. Practice Growth & Technical Evangelism: Lead technical proposals (RFPs/POCs), formulate point-of-view (PoV) whitepapers, explore emerging frontier models and open-source tooling, and continuously enhance internal IP, reference architectures, and reusable accelerators. Mentorship & Technical Leadership: Foster engineering excellence by conducting rigorous code/design reviews, guiding junior scientists on applied ML/GenAI methodologies, and enforcing reproducible data science standards. What We Are Looking For You are an applied AI practitioner and pragmatic engineer who thrives on turning raw research and frontier models into secure, reliable software systems. You balance algorithmic depth with system architecture, understand the operational bottlenecks and scaling best practices and are able to own technical delivery at scale end to end. Minimum Qualifications Education & Experience: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field, paired with 4+ years of hands-on experience in applied machine learning, deep learning, and advanced NLP/GenAI in an enterprise or consulting environment. GenAI & Agentic Architectures: Deep hands-on experience building autonomous agents and complex multi-agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, LlamaIndex, Semantic Kernel) using tool-calling, stateful memory, and routing graphs. Advanced RAG & Retrieval Systems: Demonstrated expertise implementing hybrid search pipelines (dense vectors + sparse BM25), semantic routing, contextual compression, reranking models (e.g., Cohere, BGE), and production vector databases (e.g., Pinecone, Qdrant, Milvus, pgvector). Fine-Tuning & Model Adaptation: Practical experience fine-tuning open-source LLMs/SLMs (e.g., Llama, Mistral, Gemma, Phi) using PEFT, LoRA/QLoRA, DeepSpeed, or Unsloth, paired with robust knowledge of post-training evaluation frameworks (e.g., Ragas, DeepEval, TruLens). Software Engineering & APIs: Strong proficiency in Python, modern asynchronous web frameworks (FastAPI), containerization (Docker, Kubernetes), and designing secure, low-latency REST/gRPC endpoints. Cloud & Distributed Computing: Hands-on experience architecting ML/AI workloads on at least one major cloud platform (AWS SageMaker/Bedrock, Azure OpenAI/ML, or GCP Vertex AI). Where you’ll be working In this hybrid role, you'll have a defined work location that includes work from home and assigned office days set by your manager. Scheduled Weekly Hours 40 Equal Employment Opportunity Verizon is an equal opportunity employer. We evaluate qualified applicants without regard to race, gender, disability or any other legally protected characteristics.