Princ Engr-ai Science

Verizon Verizon · Telecom · Chennai, India +1

Principal Engineer focused on architecting, developing, and deploying end-to-end, AI-powered commercial applications. This role involves integrating AI models (LLMs, predictive) into full-stack applications, from UI to backend and infrastructure, with a focus on scalability and MLOps best practices. The goal is to industrialize AI capabilities across Verizon, impacting decisions and user experiences.

What you'd actually do

  1. Lead the full-stack development of next-generation AI applications, ensuring seamless integration between complex AI models (LLMs, predictive models) and modern web interfaces.
  2. Design and deploy scalable APIs and microservices to serve machine learning models using modern frameworks (e.g., FastAPI, Node.js, GraphQL).
  3. Develop intuitive, responsive front-end interfaces (React, Next.js, Vue) that allow users to interact with AI models effectively.
  4. Implement robust backend architectures incorporating traditional and modern databases (PostgreSQL, MongoDB, and Vector Databases like Pinecone or Milvus for RAG architectures).
  5. Test and validate AI methods, ensuring privacy, ethical modeling practices, and bias-free implementations.

Skills

Required

  • Full Stack Development: Expertise in modern backend languages and frameworks (Python/FastAPI, Node.js, Go).
  • Strong proficiency in modern frontend frameworks (React, Next.js, TypeScript/JavaScript, TailwindCSS).
  • AI/ML & Data Engineering: Deep understanding of underlying math/algorithms combined with expertise in ML frameworks (PyTorch, TensorFlow) and ML libraries (Scikit-learn, Hugging Face).
  • Hands-on experience with modern AI stacks (LangChain, LlamaIndex, OpenAI APIs) and Vector Databases.
  • Experience with building and maintaining streaming and batch data pipelines (Kafka, Spark) in production.
  • Strong knowledge of cloud platforms (AWS, GCP) and container orchestration (Docker, Kubernetes).
  • Core Competencies: Exposure to advanced data structures, data modeling, and modern software architecture patterns.

Nice to have

  • Excellent cross-functional collaboration and communication skills, with the ability to explain complex code and math to non-technical stakeholders.
  • Outstanding analytical and problem-solving skills.

What the JD emphasized

  • critical that this candidate possesses proven experience in not just researching AI techniques, but actively engineering the scalable, full-stack applications that bring them to life
  • Experience independently developing algorithms, writing production-grade code, and deploying complex software/AI solutions to production.
  • Experience scaling AI in commercial applications (e.g., Generative AI, Large Language Models (LLMs), Personalization, Computer Vision) and integrating them into full-stack applications.

Other signals

  • industrialize our data science and AI capabilities
  • AI will fuel all decisions and user experiences
  • design and shape full-stack AI at scale
  • engineering the scalable, full-stack applications that bring them to life